Segmentation Phase Research Articles

HACDRAN: A Novel Hybrid Atrous Convolution Model With Densenet and Residual Attenuation Network for Multi‐Plant Leaf Disease Classification

ABSTRACTThe beneficiation of the plant is majorly crucial for both the environment and human life. The crops do trouble from disorders like other species. Various plant leaf disorders happen and trouble the general growth of the crop. These crop leaf disorders are troubles the entire plant consisting flower, root, stem, and leaf. The plant leaf disorders are mostly not taken care of by the farmers so that the crop dies or can create the cause of fruits, flowers and leave a drop. Significant detection of these disorders is essential for detecting and takes preventive measures of plant leaf disorders. The research of plant leaf disorders, procedures, and their causes for handling and managing is known as plant pathology. However, the conventional model encloses the involvement of humans in the identification and categorization of crop leaf disorders. This approach is expensive and consumes more time. It is highly essential to design a new multi‐plant leaf disorder categorization model to tackle the above‐mentioned complexities. At first, the multi‐plant leaf pictures are obtained from online sources and they are offered to the abnormality segmentation phase. Here, the abnormality presented in the multi‐plant leaf image is segmented by adaptive TransUNet, where the parameters are optimized by the developed Mean Position of Sheep Flock and Cuckoo Search (MPSFCS). Further, the abnormality segmented images are offered to the multi‐plant leaf disease classification phase. In this phase, the multi‐plant leaf disorders are categorized by utilizing Hybrid Atrous Convolution based Networks of DenseNet and Residual Attenuation Network (HACDRAN), and their parameters are optimized by the developed MPSFCS to offer effective multi‐plant leaf disease classified outcome. Thus, the developed multi‐plant leaf disease classification model provides a better functionality rate than the conventional methods with multiple analyses.

Whole-Body and Segmental Phase Angles and Cognitive Function in the Older Korean Population: Cross-Sectional Analysis.

Recently, the phase angle (PhA) has emerged as an essential indicator of cellular health. Most studies have examined its association with physiological conditions, such as sarcopenia, frailty, and physical function, in older populations. Simultaneously, growing attention is being paid to the clinical relevance of segmental PhAs for future applications. However, few studies have explored the relationship between PhAs, especially segmental PhAs, and the psychological aspects of health, particularly cognitive function. We aimed to investigate the association between whole-body and segmental PhAs and cognitive function in older adults. Individuals aged 65 years and above were recruited from adult community groups residing in Busan, South Korea, through the 2022 Bus-based Screening and Assessment Network (BUSAN) study of Pusan National University Hospital. Participants' whole-body and segmental PhAs were measured using a bioelectrical impedance analyzer (BWA 2.0 Body Water Analyzer, InBody), and cognitive functions (overall and subdomains, including memory, orientation, attention and calculation, and language) were self-reported using the Korean version of the Mini-Mental State Examination. Multiple linear regression analyses were performed to examine these associations. This study included 625 older adults aged 65-96 years (women: n=444, 71%; men: n=191, 29%). A positive association was observed between whole-body PhA and cognitive function (b=0.62, 95% CI 0.16-1.08; P<.01). We observed significant positive associations between the PhA of the lower limbs (b=0.72, 95% CI 0.38-1.06; P<.001) and cognitive function. Analysis of the Mini-Mental State Examination subdomains revealed that whole-body PhA was significantly related to memory (b=0.11, 95% CI 0.00-0.22; P=.04); the PhA of the upper limbs was significantly related to orientation (b=0.29, 95% CI 0.09-0.49; P=.01); and the PhA of the lower limbs was significantly related to orientation (b=0.24, 95% CI 0.10-0.38; P<.001), attention and calculation (b=0.21, 95% CI 0.06-0.37; P=.01), memory (b=0.14, 95% CI 0.05-0.22; P=.001), and language functions (b=0.07, 95% CI 0.01-0.12; P=.01). However, trunk PhA showed no significant association. Our findings bolster the emerging evidence of a significant positive correlation between whole-body PhA and cognitive function in our sample, with nuanced relationships observed across different segmental PhAs and cognitive subdomains. Therefore, this study revealed that PhAs could be a useful tool for screening or preventing cognitive decline in the general older population, offering substantial evidence for future interventional studies. Further research should delve into the mechanisms and assess targeted interventions that enhance regional physical function to support cognitive health in older adults. Further long-term investigation on these associations is warranted.

Deep learning-based recognition of construction activities in real construction site environment

PurposeToday’s technological advancements have had a significant impact on the construction industry. Managing and controlling complex construction projects has been made significantly easier using technological tools. One such advancement is the automatic identification of workers’ activities. This study aims to classify construction worker activities by analyzing real-time motion data collected from sensors.Design/methodology/approachIn accordance with our specific goals, we utilized advanced deep-learning methodologies such as deep neural networks, convolutional neural network, long short-term memory and convolutional long short-term memory to analyze the data thoroughly. This involved experimenting with various window sizes and overlap ratios to determine the optimal combination that would result in the most accurate predictions.FindingsBased on the analysis results, the convolutional long short-term memory (ConvLSTM) deep learning model with a window size of 4.8 s and an overlap rate of 75% was found to be the most accurate prediction model. This model correctly predicted 98.64% of the basic construction worker activities in a real construction site environment.Originality/valuePrevious studies have mainly been conducted in laboratory environments and have focused on basic construction activities such as lifting, moving, sawing and hammering. However, this study collected data from real workers in a real construction site environment. Various deep learning models were employed to determine the most accurate one. Additionally, several options were tested to determine the optimal window size and overlap ratio during the data segmentation phase, aiming to select the most suitable ones for preparing the data for the model.

Relationship between characteristics of segmental phase angles and walking ability in patients with hemiplegia after stroke and traumatic brain injury in a convalescent rehabilitation ward.

This study determined the characteristics of the phase angles (PhAs) of the trunk, affected limb, and unaffected limb at admission and their relationship with walking ability at discharge in patients with stroke and traumatic brain injury in a convalescent rehabilitation ward. Stroke and traumatic brain injury survivors with hemiplegia admitted to a convalescent rehabilitation ward were divided into independent- and dependent-walking groups. The trunk, affected limb, and unaffected limb PhAs were evaluated using bioelectrical impedance analysis and compared between the independent- and dependent-walking groups. Friedman's test was performed to analyze differences in the trunk, affected limb, and unaffected limb PhAs within each group. Multiple regression was performed to examine the association between walking ability and segmental PhAs. We enrolled 47 patients (independent-walking group: n = 12; dependent-walking group: n = 35). The dependent-walking group had a significantly lower trunk, affected limb, and unaffected limb PhAs. The segmental PhA was higher in the trunk than in the affected and unaffected limbs in the independent-walking group and lower in the affected limb than in the trunk and unaffected limb in the dependent-walking group. Only the trunk PhA at admission was significantly associated with walking ability at discharge ( β = 0.367; P = 0.002). The trunk PhA at admission may be related to walking ability at discharge in patients undergoing stroke and traumatic brain injury rehabilitation.

St-RegSeg: an unsupervised registration-based framework for multimodal magnetic resonance imaging stroke lesion segmentation.

Stroke is one of the leading causes of disability and death worldwide. Ischemic stroke accounts for 75-90% of all stroke incidents. Assessing the size and location of the stroke lesion is crucial for treatment decisions, especially those related to urgent vascular reconstruction surgery. Magnetic resonance imaging (MRI) offers excellent soft tissue contrast and multimodal imaging characteristics, which can reflect changes in the physiological functions of brain soft tissues in patients with ischemic stroke. However, using deep learning (DL) techniques for MRI segmentation of stroke lesions still faces many challenges. On the one hand, single-modal segmentation models cannot effectively integrate multimodal information; on the other hand, there is a semantic content drift between multimodal stroke MRI images, leading to lower accuracy in subsequent multimodal image segmentation. To address these issues, we aimed to propose the stroke unsupervised registration and segmentation (St-RegSeg) framework. The St-RegSeg framework integrates an unsupervised registration model, ConvNXMorph, and a segmentation model, nnUNet-v2, enabling both registration and segmentation of multimodal MRI images. The St-RegSeg framework was evaluated on the ISLES'22 dataset from three centers. The St-RegSeg framework demonstrated significant improvements in performance metrics and computational efficiency. Compared to advanced normalization tools (ANTs) [symmetric normalization (SyN)] + nnUNet-v2, the St-RegSeg framework improved the Dice similarity coefficient (DSC) by 25.31% in the registration phase, reduced mean squared error (MSE) by 17.36%, increased normalized cross-correlation (NCC) by 16.06%, and enhanced mutual information (MI) by 17.09%. Additionally, in the segmentation phase, it increased the DSC by 0.84%, and the overall inference speed was increased by 40.91 times. Compared to the suboptimal TransMorph + nnUNet-v2, the St-RegSeg framework improved the DSC by 3.68% in the registration phase, reduced MSE by 8.91%, increased NCC by 8.49%, enhanced MI by 6.18%, and in the segmentation phase, it raised the DSC by 0.5%, with the overall inference speed increased by 2.13 times. The St-RegSeg framework provides a highly effective solution for the registration and segmentation of multimodal MRI images in ischemic stroke cases. Its performance metrics and computational efficiency significantly outperform existing methods, making it a promising tool for clinical applications. The code is open-sourced and available at: https://github.com/Cooper-Gu/St-RegSeg.

Spatial pedagogy: exploring semiotic functions of one teacher’s movement in an Active Learning Classroom

Abstract A teacher’s use of classroom space via embodied movement enacts a specific spatial pedagogy and has a significant impact on the nature of teaching and learning that can take place. The ubiquity and large expenditure in renovating university learning environments have not been accompanied by well-informed research due to a tendency to background embodied movement in pedagogic investigations. This paper explores the semiotic potentials and pedagogic functions of one teacher’s embodied movement in an Active Learning Classroom in a tertiary setting. It maps out the interrelationship of embodied movement and space as an entanglement of materiality and practice, which complements existing research that relates movement to space only in terms of layout and composition. By conducting a nuanced multimodal analysis of one teacher’s co-patterns of movement and speech in a tertiary lesson, this paper finds that co-patterns of embodied movement and speech in the classroom can segment phases of pedagogic discourse into five secondary phases and that these phases congruently enact different types of space to modulate the teacher-student relationship and enact different pedagogic roles. The paper concludes with a discussion of the implication of embodied movement on classroom design and pedagogic practice.

Automatic screening of retinal lesions for detecting diabetic retinopathy using adaptive multiscale MobileNet with abnormality segmentation from public dataset

ABSTRACT Owing to the epidemic growth of diabetes, ophthalmologists need to examine the huge fundus images for diagnosing the disease of Diabetic Retinopathy (DR). Without proper knowledge, people are too lethargic to detect the DR. Therefore, the early diagnosis system is requisite for treating ailments in the medical industry. Therefore, a novel deep model-based DR detection structure is recommended to fix the aforementioned difficulties. The developed deep model-based diabetic retinopathy detection process is performed adaptively. The DR detection process is imitated by garnering the images from benchmark sources. The gathered images are further preceded by the abnormality segmentation phase. Here, the Residual TransUNet with Enhanced loss function is used to employ the abnormality segmentation, and the loss function in this structure may be helpful to lessen the error in the segmentation procedure. Further, the segmented images are passed to the final phase of retinopathy detection. At this phase, the detection is carried out through the Adaptive Multiscale MobileNet. The variables in the AMMNet are optimized by the Adaptive Puzzle Optimization to obtain better detection performance. Finally, the effectiveness of the offered approach is confirmed by the experimentation procedure over various performance indices.

Segmental phase angles as predictors of functional recovery and activities of daily living in patients after stroke.

This study investigated the association between segmental phase angles and functional outcomes in patients after stroke, hypothesizing that increased segmental phase angle correlates with improved functional status. A retrospective cohort study of 1012 patients after stroke was conducted. Whole body and segmental phase angles were measured using bioelectrical impedance analysis within 3 days of admission. Our exposure of interest was segmental phase angle measured via a multifrequency bioelectrical impedance analyzer and calculated as phase angle = arctangent (Xc/R) × (180/π), where R is the resistance of the right half of the body and Xc is the reactance measured at 50 kHz. The primary outcomes were the motor subscale of the functional independence measure (FIM) at discharge and FIM change between admission and discharge. Secondary outcomes included FIM scores for specific activities. Multiple linear regression analysis was performed to assess associations. Phase angles of the healthy upper and lower limbs demonstrated stronger associations with discharge FIM motor scores (β = 0.175 and β = 0.105, respectively) and FIM motor gain (β = 0.242 and β = 0.092, respectively) compared with whole body or paretic limb phase angles. Upper limb phase angles were more closely related to grooming and toileting abilities, whereas lower limb phase angles were associated with both toileting and locomotion at discharge. Segmental phase angles, particularly those of the nonparetic limbs, are promising predictors of functional outcomes in patients after stroke. Assessing segmental phase angles may guide targeted interventions and rehabilitation strategies for improving specific activities of daily living.

A novel approach for detection and classification of re-entrant crack using modified CNNetwork

PurposeIn the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse.Design/methodology/approachIn the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method.FindingsIn the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results.Originality/valueThe originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.

Early embryonic development of the German cockroach Blattella germanica

BackgroundEarly embryogenesis is characterized by dramatic cell proliferation and movement. In most insects, early embryogenesis includes a phase called the uniform blastoderm, during which cells evenly cover the entirety of the egg. However, the embryo of the German cockroach, Blattella germanica, like those of many insects within the super order Polyneoptera, does not have a uniform blastoderm; instead, its first cells condense rapidly at the site of a future germband. We investigated early development in this species in order to understand how early gene expression is or is not conserved in these insect embryos with distinct early cell behaviors.ResultsWe present a detailed time series of nuclear division and distribution from fertilization through germband formation and report patterns of expression for the early patterning genes hunchback, caudal, and twist in order to understand early polarization and mesoderm formation. We show a detailed time course of the spatial expression of two genes involved in the segmentation cascade, hedgehog and even-skipped, and demonstrate two distinct dynamics of the segmentation process.ConclusionsDespite dramatic differences in cell distribution between the blastoderms of many Polyneopteran insects and those of more well-studied developmental models, expression patterns of early patterning genes are mostly similar. Genes associated with axis determination in other insects are activated relatively late and are probably not maternally deposited. The two phases of segmentation—simultaneous and sequential—might indicate a broadly conserved mode of morphological differentiation. The developmental time course we present here should be of value for further investigation into the causes of this distinct blastoderm type.

Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.

Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to various significant issues, including lesion variations in texture, shape, color, and size; artifacts (hairs); uneven lesion boundaries; and poor contrast. To solve these issues, this research proposes a novel Convolutional Swin Transformer (CSwinformer) method for segmenting and classifying skin lesions accurately. The framework involves phases such as data preprocessing, segmentation, and classification. In the first phase, Gaussian filtering, Z-score normalization, and augmentation processes are executed to remove unnecessary noise, re-organize the data, and increase data diversity. In the phase of segmentation, we design a new model "Swinformer-Net" integrating Swin Transformer and U-Net frameworks, to accurately define a region of interest. At the final phase of classification, the segmented outcome is input into the newly proposed module "Multi-Scale Dilated Convolutional Neural Network meets Transformer (MD-CNNFormer)," where the data samples are classified into respective classes. We use four benchmark datasets-HAM10000, ISBI 2016, PH2, and Skin Cancer ISIC for evaluation. The results demonstrated the designed framework's better efficiency against the traditional approaches. The proposed method provided classification accuracy of 98.72%, pixel accuracy of 98.06%, and dice coefficient of 97.67%, respectively. The proposed method offered a promising solution in skin lesion segmentation and classification, supporting clinicians to accurately diagnose skin cancer.

Pulmonary Emphysema Disease Classification Model Using Deep Structured Techniques

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance.

CNN Based Determination of Polycystic Ovarian Syndrome using Automatic Follicle Detection Methods

The aim of this study was to determine the best method for follicle detection using ovarian ultrasound images and to classify the ultrasound images as pcos or normal ovaries using the proposed CNN architecture. Two different methods for follicle detection have been proposed to evaluate pcos. For this purpose, the Median, the Mean, the Wiener, and the Gaussian filters were tested using standard and adaptive thresholds. Second, Gaussian filtering, Discrete Wavelet Transform, and k-means clustering algorithms were tested. The Canny operator separates follicles from the background in the segmentation phase. In this study, a CNN architecture that classifies limited ultrasound ovary images was developed, and its success in the best follicle detection method was presented. The highest follicle detection accuracy of 97.63% was achieved with adaptive thresholding using a Wiener filter. Besides, the ultrasound images of the ovaries were classified as "normal" or "polycystic ovary syndrome" using CNN architecture with classification accuracy of 65.81% for unsegmented ovarian images and 77.81% for segmented images. In addition to the proposed method, classification was performed using SqueezeNet-based transfer learning, which was successful in limited datasets, and 74.18% classification accuracy was achieved for the unsegmented images and 75.54 % for segmented images . The results show that the combination of the Wiener filter with adaptive thresholding was quite successful in follicle detection and that the CNN can better classify ovaries using preprocessed ultrasound images.

Liver cancer classification via deep hybrid model from CT image with improved texture feature set and fuzzy clustering based segmentation

One of the leading causes of death for people worldwide is liver cancer. Manually identifying the cancer tissue in the current situation is a challenging and time-consuming task. Assessing the tumor load, planning therapies, making predictions, and tracking the clinical response can all be done using the segmentation of liver lesions in Computed Tomography (CT) scans. In this paper we propose a new technique for liver cancer classification with CT image. This method consists of four stages like pre-processing, segmentation, feature extraction and classification. In the initial stage the input image will be pre processed for the quality enhancement. This preprocessed output will be subjected to the segmentation phase; here improved deep fuzzy clustering technique will be applied for image segmentation. Subsequently, the segmented image will be the input of the feature extraction phase, where the extracted features are named as Improved Gabor Transitional Pattern, Grey-Level Co-occurrence Matrix (GLCM), Statistical features and Convolutional Neural Network (CNN) based feature. Finally the extracted features are subjected to the classification stage, here the two types of classifiers used for classification that is Bi-GRU and Deep Maxout. In this phase we will apply the Crossover mutated COOT optimization (CMCO) for tuning the weights, So that we will improve the quality of the image. This proposed technique, present the best accuracy of disease identification. The CMCO gained the accuracy of 95.58%, which is preferable than AO = 92.16%, COA = 89.38%, TSA = 88.05%, AOA = 92.05% and COOT = 91.95%, respectively.

Comparing Device-Generated and Calculated Bioimpedance Variables in Community-Dwelling Older Adults.

Despite BIA emerging as a clinical tool for assessing older adults, it remains unclear how to calculate whole-body impedance (Z), reactance (Xc), resistance (R), and phase angle (PhA) from segmental values using modern BIA devices that place electrodes on both sides of the body. This investigation aimed to compare both the whole-body and segmental device-generated phase angle (PhADG) with the phase angle calculated using summed Z, Xc, and R from the left, right, and combined sides of the body (PhACalc) and to compare bioelectric variables between sides of the body. A sample of 103 community-dwelling older adults was assessed using a 50 kHz direct segmental multifrequency BIA device. Whole-body PhACalc values were assessed for agreement with PhADG using 2.5th and 97.5th quantile nonparametric limits of agreement and Spearman's rho. Bioelectrical values between sides of the body were compared using Wilcoxon rank and Spearman's rho. A smaller mean difference was observed between PhADG and right PhACalc (-0.004°, p = 0.26) than between PhACalc on the left (0.107°, p = 0.01) and on the combined sides (0.107°, p < 0.001). The sum of Z, R, and PhACalc was significantly different (p < 0.01) between the left (559.66 ± 99.55 Ω, 556.80 ± 99.52 Ω, 5.51 ± 1.5°, respectively) and the right sides (554.60 ± 94.52 Ω, 552.02 ± 94.23 Ω, 5.41 ± 0.8°, respectively). Bilateral BIA values do not appear to be interchangeable when determining whole-body measurements. Present data suggest that using right-sided segmental values would be the most appropriate choice for calculating whole-body bioelectrical variables.

Water Wheel Plant Dingo Optimizer enabled Deep Convolutional Neural Network for disease detection using hyperspectral leaf image

ProblemIn many countries, agriculture is themain sourceofpeople’s livelihoodandsatisfiestheir nutritional needs.Earlydetection ofplantdiseases throughagriculturalremote monitoringis important to prevent the disease’s spread. The traditional methods require sampling and can damage the plant, but hyperspectral imaging is non-destructive. AimThe major aim of this research is to devise a Water Wheel Plant Dingo Optimizer_Deep Convolutional Neural Network (WWPDO_Deep CNN) for disease detection using a hyperspectral leaf image. MethodsInitially, the input leaf image is given into the leaf segmentation phase, which is done using the proposed Water Wheel Plant Dingo Optimizer (WWPDO), which is the amalgamation of the Water Wheel Plant Algorithm (WWPA) and Dingo Optimizer (DOX). The selected bands’ outputs are subjected to leaf segmentation and which is carried out by employing Bayesian Fuzzy Clustering (BFC). Thereafter, leaf segmented outputs are fussed using the majority voting method. Fused output and individual leaf segmentation output are given into the feature extraction process to extract features, such as local binary patterns and Weber local descriptors. Finally, leaf disease detection is performed using a deep Convolutional Neural Network (Deep CNN) for normal and abnormal cases. The hyperparameters of the Deep CNN are fine-tuned based on the proposed WWPADO. ResultsThe proposed WWPDO_Deep CNN achieved an excellent performance with an accuracy of 91.35 %, a True Positive Rate (TPR) of 93.13 % and a True Negative Rate (TNR) of 90.76 %. ConclusionThe WWPDO_Deep CNN is applicable for early diagnosis under the new classification system and provides a new direction for early diagnosis based on hyperspectral images. Also, the devised model provides an accurate diagnosis of plant diseases. Early and accurate detection allows targeted treatment, reduces the need for widespread pesticide application and promotes more sustainable farming practices.

ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)”. This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

Barnacle-inspired amphipathic high strength adhesives under-water/oil

Adhesives designed for bonding in liquid environments have a broad application prospect. In general, amphiphilic polymer structures are important research objects for underwater bonding. And in the study of underwater adhesives, it was found that they can quickly dispel or absorb interfacial water and form reliable bulk strength and adhesion strength. However, achieving strong adhesion in both water-based and oily environments poses a significant challenge. In this work, an adhesive inspired by barnacles that can meet the bonding requirements in both water-based and oily environments has been prepared. Hydrophilic (polyethylene glycol (PEG)) main chains and hydrophobic (1-Octadecyl thiol (ODT)) side chains are simultaneously integrated into the polyurethane structure. Different from the past, hydrophobic and hydrophilic segments are included in the hard segment and soft segment microregion, respectively; At the same time, both hydrophilic and hydrophobic chain segments have strong crystallization, which greatly strengthens the driving force of molecular segments migration and phase separation compared with ordinary hydrophilic/hydrophobic structures. Water-based and oil-based solvents drive hydrophilic and hydrophobic molecular chains to diffuse to the surface, respectively, which enhances the adhesive strength to hydrophilic and hydrophilic surfaces, respectively. In comparison to the adhesion strength observed in air, the adhesion strength of the adhesive to polymethyl methacrylate (PMMA) increases by 1.72 times in oil environments (from 1.02 to 1.75 MPa). Similarly, the adhesion strength to aluminum (Al) increases by 2.23 times under water (from 1.82 to 4.04 MPa). These findings suggest that the regulation strategy for polymer chains employed in this study holds great potential for advancing adhesive performance in diverse environments.

Novel Deep CNNs Explore Regions, Boundaries, and Residual Learning for COVID-19 Infection Analysis in Lung CT.

COVID-19 poses a global health crisis, necessitating precise diagnostic methods for timely containment. However, accurately delineating COVID-19-affected regions in lung CT scans is challenging due to contrast variations and significant texture diversity. In this regard, this study introduces a novel two-stage classification and segmentation CNN approach for COVID-19 lung radiological pattern analysis. A novel Residual-BRNet is developed to integrate boundary and regional operations with residual learning, capturing key COVID-19 radiological homogeneous regions, texture variations, and structural contrast patterns in the classification stage. Subsequently, infectious CT images undergo lesion segmentation using the newly proposed RESeg segmentation CNN in the second stage. The RESeg leverages both average and max-pooling implementations to simultaneously learn region homogeneity and boundary-related patterns. Furthermore, novel pixel attention (PA) blocks are integrated into RESeg to effectively address mildly COVID-19-infected regions. The evaluation of the proposed Residual-BRNet CNN in the classification stage demonstrates promising performance metrics, achieving an accuracy of 97.97%, F1-score of 98.01%, sensitivity of 98.42%, and MCC of 96.81%. Meanwhile, PA-RESeg in the segmentation phase achieves an optimal segmentation performance with an IoU score of 98.43% and a dice similarity score of 95.96% of the lesion region. The framework's effectiveness in detecting and segmenting COVID-19 lesions highlights its potential for clinical applications.

Ring-shaped segmentation phase design method for the combined point spread function.

The study presents a method for designing phase masks, specifically the ring-shaped segmentation method, which can be employed in creating the modulation phase for specialized point spread functions (PSFs), such as multi-focus PSFs and those with axial encoding functions. An algorithm for phase inversion optimization is introduced to enhance the optical transfer function efficiency of the designed phase mask, which is based on the Fresnel approximation imaging inverse operation and iterative Fourier transform algorithm. The ring-shaped segmentation phase design approach effectively combines individual phases, resulting in unified PSFs with unique properties. The promising outcomes demonstrated by the designed PSFs are truly remarkable. The refined phase masks and experimental verification further validate the effectiveness of this groundbreaking approach. This advancement in ring-shaped segmentation method development has significant potential for real-world applications, representing a noteworthy contribution to the field of optical imaging.