Normal Pictures Research Articles

In Situ Diagnosis and Digital Cataloguing of Plant Pathogenic Fungi Through Mobile‐Based Foldscope Microscopy

ABSTRACTAgriculture confronts multifaceted challenges across the spectrum of crop production, with pest and disease management being a prominent concern. Timely diagnosis of crop diseases is imperative for mitigating production costs and curbing the adverse environmental impacts of chemical pesticides. In the present investigation, mobile phone‐based foldscope microscopy (MBFM) was used to diagnose various field samples infected with fungal diseases of field and horticultural crops, and the same was validated with the normal microscope pictures and field symptoms. The MBFM was also used to diagnose seed‐borne microflora associated with wheat and spores of commercial formulation of bioagents and validated. The MBFM utilises both symptoms and morphological structures of pathogen for in situ field diagnosis and hence advantages over the symptom‐based mobile Apps. This study underscores the utility of foldscope microscope as a potent technique for plant pathologists and extension workers to enable real‐time and in situ identification of diseases caused by fungal pathogens in various agricultural crops.

Explainable optimization of deep learning model for COVID-19 detection using chest images

The COVID-19 pandemic has now become endemic, yet it remains essential to identify and diagnose the virus. The X-ray images on chest evaluations have utilized numerous deep-learning techniques and optimization algorithms. The Visual Geometry Group (VGG) is a widely recognized deep-learning architecture for COVID-19 detection. This architecture incorporates various optimization algorithms, such as Adadelta, Adam, Adamax, Stochastic Gradient Descent (SGD), Adagrad, Adam, and Root Mean Square Propagation (RMSprop), to ascertain the most favorable learning parameters. Currently, researchers evaluate the performance of the deep learning and optimization algorithm to detect COVID-19 by computing the statistical parameters, i.e., the accuracy, precision, recall, specificity, and F-1 score. However, the way in which deep learning and optimization algorithms work to predict the outcome still needs a comprehensive explanation. This study aims to explain how deep learning and optimization predict COVID-19 or normal chest using visual and quantitative analysis of a Gradient Class Activation Mapping (Grad-CAM) and statistical evaluation. The methodology involved data collection of chest images from a public data set, preprocessing, training-testing, and result analysis on statistical parameters and Grad-CAM heatmap. The evaluation of statistical parameters yielded favorable outcomes from all the optimization techniques, with an average accuracy of up to 99 %. It was then followed by visually observing the Grad-CAM activation heatmap, which indicated crucial regions in the images that influenced the model's prediction outcomes. Grad-CAM allows the visualization and quantification of the activation map in each optimization. It reveals distinct activation maps for the lung area in both COVID-19 and normal pictures. The visual observation was also confirmed by quantitative analysis of Grad-CAM using root mean square error (RMSE) of correlation between each optimization algorithm. The researcher compares two heatmaps from different optimization methods by computing the RMSE of each heatmap pixel value. The RMSE confirmed that the heatmap supports the explanation of the optimization performance. In conclusion, this research explained how deep learning and optimization algorithms predict COVID-19 from the chest images.

Ensemble Learning-based Multiple Sclerosis Detection Technique Using Magnetic Resonance Imaging

Multiple sclerosis (MS) is a complicated neurological disorder that leads to demyelination of nerve fibers in the central nervous system, causing severe symptoms and gradual impairment. Prompt and precise diagnosis of MS is essential for prompt intervention and individualized treatment planning. This research presents a new method for detecting MS: magnetic resonance imaging (MRI) data. Utilizing current progress in deep learning and ensemble learning methodologies, we use SWIN transformer and MobileNetV3-small for extracting features from MRI images. These features are then used for classification using CatBoost, XGBoost, and random forest algorithms. The suggested framework is tested and confirmed effective using the Kaggle MS database, which consists of various MRI images. The experimental findings show a remarkable average accuracy of 99.8% and a little loss of 0.07, highlighting the effectiveness of the suggested strategy in discriminating between aberrant and normal MRI pictures that indicate MS. This study enhances the field of medical image analysis by providing a precise and effective framework for automated diagnosis of MS. This framework has the potential to enhance diagnostic efficiency and improve patient outcomes. Combining deep learning feature extraction with ensemble classifiers offers a robust and easily understandable approach for diagnosing MS and has the potential to be used in clinical settings. Future research should prioritize validating the suggested technique on more extensive datasets and incorporating it into clinical practice to enhance early identification of MS and provide individualized patient treatment.

Creating a deep learning model using a Swin Transformer and tree growth optimisation to classify brain tumour

The brain, which has billions of cells, is the largest and most complex organ in the human body. A brain tumor is the primary malignant intracranial tumor of the central nervous system that develops most frequently. They are frequently found too late for effective therapy. The use of minimally invasive procedures is necessary to make a diagnosis and monitor a tumor of the central nervous system's response to therapy. There exist three distinct classifications of tumors, namely benign, premalignant, and malignant. This study concentrated on using deep learning to identify brain tumors (BT) using normal or abnormal brain pictures. Numerous methodologies have been employed to augment the quality of images, encompassing image smoothing and noise restoration procedures. The present study employs the proposed Adaptive Weighted Frost filter as it has been identified as the optimal approach for noise reduction in BT photographs. The Swin Transformer technology is employed for the purpose of classifying the BT. The efficiency of the Tree Growth Optimization (TGA) model for Swin transformer hyper parameter tweaking has been evaluated in this work. Before using our unique BT dataset for extensive experimental comparisons, medical specialists carefully examined it down to the pixel level. The predicted model achieved the greatest F1 score of 99.82% and the maximum accuracy, recall, and 100%, respectively.

Evaluation of deep learning models for melanoma image classification

Melanin-producing cells are the origin of melanoma, the worst form of skin cancer (Melanocytes). If this cancer is not caught early, it might spread to other organs. With automated diagnostic technologies, clinicians and non- professionals may better diagnose diseases. Dermoscopic analysis, biopsy, and histological tests may be needed starting with a clinical assessment. Photo-based skin lesion categorization is challenging due to the fine-grained variability of skin lesions. We provide a more reliable melanoma detection model for each suspicious lesion in this paper. A set of characteristics characterizing a skin lesion's borders, texture, and coloursis used to educate convolutional neural networks. The deep learning models were generated using a standard dataset. To know the model's performance, consider the metrics like accuracy, sensitivity, specificity, Jaccard index and Dice coefficient. Transfer learning is used to categorize normal and diseased skin pictures automatically. This model-driven design helps doctors swiftly assess lesions.

PSO Optimized CNN-SVM Architecture for Covid -19 Classification

This paper presents a hybrid model that utilizes PSO particle swarm optimization, Convolution Neural Networks (CNN) and (SVM) Support Vector Machine architecture for recognition of Covid19.The planned model extracts optimized structures with particle swarm optimization then passes to Convolution Neural Network for automatic feature extraction, while the SVM serves as a Multi classifier. The dataset comprises Covid 19, Pneumonia and Normal Chest X-Ray pictures used to hone and evaluate the suggested algorithm. The most distinct traits are automatically extracted by the algorithm from these photographs. Experimental results show that the suggested framework is effective, with an average recognition accuracy of 97.42%.The most successful SVM Kernel was RBF.

Analgesic and anti-inflammatory effects of oral Centella asiatica (L.) urban extract in cats undergoing ovariohysterectomy

BackgroundCentella asiatica Linn Urban (CA) is a medicinal herb used in traditional medicine for its anti-inflammatory and antinociceptive properties. Feline species usually exhibit toxicity toward nonsteroidal anti-inflammatory drugs, and thus the analgesic potential of CA is worth investigating in domestic cats. PurposeThe objective of this study was to evaluate the efficacy of CA extract in oral formulation for controlling postoperative pain and inflammation in cats undergoing ovariohysterectomy. Study DesignRandomized, prospective, blind, placebo-controlled study. MethodsThis study included 18 healthy female cats randomized into three groups: control, low-dose group (100 mg/kg), and high-dose group (200 mg/kg). Accordingly, subjects were given orally administered placebo jelly or CA mixed jelly 12 h before surgery and 4, 24, 48, and 72 h after surgery. Serum amyloid A (SAA) and white blood cell count (WBC) were tested 23 h after surgery. Postoperative pain was evaluated at 4, 8, 24, 48, and 72 h after extubation by using the UNESP-Botucatu multidimensional composite pain scale (UFEPS). Wound scores were assessed using an infrared thermal imaging camera and normal pictures token at right after the surgery and at 1, 2, and 3 days after the surgery. SAA and WBC values were compared between groups via ANOVA (p < 0.05). The Scheirer-Ray-Hare test was used to analyze the ordinal score: pain score and wound-healing score (p < 0.05). ResultsThe high-dose group had a significantly lower UFEPS score than the control group (p = 0.007). In the high-dose group, few cats had a high temperature at the surgical wound after surgery. The level of SAA, WBC, and wound scores showed no significant difference among groups. No adverse side effects were found after receiving the CA extract. ConclusionCA extract powder can be used as an analgesic in domestic cats for preventing and treating postoperative pain. The dosage and frequency of CA oral administration should be further studied.

TABLE BOOKING BY 3D TOUR

Long wait time for tables can turn customers away or can be an uncomfortable experience. Therefore, we intend to aid in a comfortable experience by bringing in a restaurant tour through our website. This involves a complete tour of the ambiance and the tables. This tour is not just looking at normal pictures of the restaurant’s ambiance to get a glimpse; it is a complete 360- degree view that helps the customer to book a table with one click. People are always running short of time and custom table booking fits right into their hectic schedule which solves the problem of waiting time. Also, this type of feature is favorable to restaurants as it saves an ample amount of time and helps in getting more customers to visit. In conclusion, a 3D tour makes booking a table easier and convenient with easy accessibility. The use of hardware tools like an android phone and a tripod will help in capturing images; programming languages like HTML, CSS, JavaScript will be used; tools like the Google Tour platform and Google Street View app will be used for producing the 360Degree video of the restaurant; and software tools like MongoDB, NodeJS, Express JS will help in building the restaurant website using Visual Studio Code.

RETRACTED: A novel similarity metric for image filtering

The image quality investigation is a significant and challenging task. One of the principal issues is the presence of noise that influences the nature of image. Denoising assume an essential part in image quality examination. The nature of the image is dissected in view of different execution measurements. This work centers on the measurement of similarity for the filtered image and the estimation is made on different normal and clinical pictures. Denoising is done by different image filtering draws closer, also the result is great for different phases of image handling like segmentation and classification approaches. An enhanced Discrete Wavelet Transform (EDWT) is utilized for image filtering and the proposed similarity measurements in view of sine measurement among the noise and the input image for image denoising and examining the nature of image. Alongside the current comparability measurements, different measurements like Peak Signal to Noise Ratio, Normalized Absolute Error, and Structural similarity Index Measure are considered for the work. The investigation is tried on various images like brain MRI scan images from the cameraman, Barbara, vegetables, remote sensing images, natural image as well as BRATS dataset,. The simulation outcome shows that the EDWT based filtering yields an improved outcome for the performance measurements.

An Exploratory Model for Face Recognition Problem in Autism: An Artificial Neural Network Approach

Studies have shown that individuals with Autism Spectrum Disorder (ASD) tend to gaze aversion during social interaction. It also has been observed that autistic people have significant problems in performing social tasks, including face recognition. Researches emphasize the role of facial gaze, especially visual communication in social interaction and learning. In this paper, we propose an Artificial Neural Network (ANN) to model ASD’s deficiency in face recognition. We used Olivetti Research Laboratory (ORL) face database and chose pictures that fitted our desires. The ANN was trained and tested in three trial experiments; in experiment 1 (exp. 1), we used pictures with up-masked faces (the upper half of the faces had been blurred) in order to model ASD’s face recognition problem, in experiment 2 (exp. 2), pictures with normal pictures was used for simulation normal individuals’ face recognition; and in experiment 3 (exp. 3), we used pictures with down-masked faces as a test group. Testing results show a 20.00% error in the exp. 1, 4.44% error in the exp. 2, and 10.00% error in the exp. 3. Based on these results, the proposed network emphasizes the face recognition problem in ASD as a result of eye contact aversion.

A Deep Learning based Advanced Gait Detection

A gait is a biometric sign that people use to provide visual verification of viewing. Distinctive highlights need more than one movement cycle. In Gait Energy Image (GEI), for example, typical images of a frame with more than one cycle motion are analyzed. The unique stride highlights the required movement of an individual strolling more than one complete walking cycle. For instance, in Gait Energy Image (GEI), normal outline pictures of more than one entire walk cycle are processed. Be that as it may, there may be an incomplete step cycle in which information is accessible due to impediments. This research work has designed and developed a model for implementing stride arrangement and profound learning strategies. A separate walk dataset is used in our trial. The walk marks are removed from the Gait Energy Image (GEI) using the Convolution Neural Network (CNN). The classifier proposed here for grouping human gait postures is Mobile Net. Finally, the proposed methodology is evaluated on the OULP step dataset, affirming that the proposed design effectively reproduces total GEI even from the outrageously deficient walk cycles.

Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques.

Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis.

Morphometry of Human Trachea in Male and Female using Computerized Tomography- A Comparative Study

Background: The architecture of the trachea can help doctors choose the right size of the endotracheal tube for patients. Non-invasive procedures like cadaveric dissection (CD) and Computed Tomography (CT) scans can also be used in the investigation. Objective: to describe the morphometry of a normal human trachea on CT scans and compare it to data from other studies Methodology: The normal CT scan pictures of 110 patients (69 males and 41 females) were analyzed in this prospective investigation. A 64-slice CT scanner was used to do chest CT scans. DICOM, a program for digital imaging and communication in medicine, was used to collect the data. For each parameter, the research population's lowest and maximum values were established. The correlation between all research variables and height and weight was determined using Pearson's correlation method. Height and weight were used as predictors in a regression analysis of substantially correlated factors. Results: In males, the AP and transverse diameters were substantially linked with height. The diameter of the AP and the CSA were found to be substantially linked with weight. A-P diameter, cross-sectional area, and volume were shown to be poorly predicted by weight. Weak predictors of length and transverse diameter were found to be height. In females, there was no association between height or weight with any of the research parameters. It was discovered that neither body mass index (BMI) nor height (or weight) could predict the dependent variables. Conclusion: The data might be valuable in the setup and design of tracheostomy tubes and endotracheal tubes in the future. Keywords: Trachea, diameters, Cross-sectional area, Volume, Computerized tomography.

Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward propagation neural network, and the ANFIS classifier is utilized to classify them. The purpose of this classifier is to distinguish between normal and pathological cervical pictures. Sensitivity is 97.52 percent, specificity is 99.46 percent, accuracy is 98.39 percent, precision is 97.48 percent, PPV is 97.38 percent, NPV is 92.27 percent, LRP is 141.81 percent, 0.0946 percent LRN, FPR is 96.82 percent, and NPR is 91.46 percent for the CC detection categorization. The proposed methodology outperforms standard CC identification and classification methodologies.

Intelligent classification of lung malignancies using deep learning techniques

PurposeComputed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.Design/methodology/approachRadiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.FindingsThe collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.Originality/valueThe proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

Machine Learning Model based Image and Video Colorization

In this paper, we present an intuitive framework for clients to effectively colorize the regular pictures of complex scenes. In our framework, colorization system is expressly isolated into two phases: Shading naming and Shading planning. Pixels that ought to generally have comparative tones are assembled into lucid districts in the shading naming stage, and the shading planning stage is then acquainted with additional tweak the tones in each intelligible locale. To deal with textures ordinarily found in normal pictures, we propose another shading marking plan that bunches not just adjoining pixels with comparative force yet additionally distant pixels with comparable surface. Propelled by the knowledge into the complementary nature moved by the exceptionally contrastive areas and the smooth areas, we utilize a perfection guide to direct the joining of power progression and surface comparability limitations in the plan of our labeling calculation. Inside each sound area acquired from the shading marking stage, the shading planning is applied to produce distinctive colorization impact by allotting shadings to a couple of pixels in the district. A bunch of natural interface apparatuses is intended for marking, shading and changing the outcome. We show convincing consequences of colorizing assuming measure of client input.

Radiographic Anatomy of the Abdomen of Local Breed Dogs

Objectives: The anatomical area that constitutes the abdomen is an area that is often subject to serious conditions. These diseases can affect the digestive and urogenital systems and radiography is a complementary examination of choice to refine the diagnosis. This study had for objective, to realize a radio anatomical atlas of the Abdomen in order to facilitate the comprehension and the interpretation of the radiographic pictures of the dog.&#x0D; Methodology: To do this, radiographs were performed on the Abdomen of 30 healthy dogs including 15 females and 15 males, (all of local breed) received at the radiology room.&#x0D; Results: At the end of the study, the best normal pictures by anatomical region and by incidence of the normal pictures of the Abdomen, were selected constituting a reference database in radiographic anatomy of this animal. Each radiograph is commented and annotated, facilitating the understanding of the images. These radiographic images will serve as a basis for the interpretation of radiographic images of the abdomen in the dog.&#x0D; Conclusion: These radiographs will serve as a basis for the interpretation of radiographic images of the abdomen of the dog.

Correlation of Depression with the Functional Profile of Elderly People Living in Homes for the Aged

Introduction: Due to the growth of the elderly population, there is a notable lack of preparation among family members and society to face the challenges of this new reality, resulting in an increasing demand for homes for the aged (HA). It is necessary to understand how the impairment of physical health, mental health and the level of depression can affect the functional capacity of the institutionalized elderly who live in homes for the aged. Objective: To describe the correlation between depression and functionality of elderly people who live in homes for the aged. Method: A cross-sectional, descriptive study with 70 elderly people of both sexes, aged 60 years or older, who live in HA in the state of Rio de Janeiro. Result: It was verified that the female gender was predominant in the HA consulted. The majority of elderly live in HA voluntarily 48.6% and that 41.43% did not complete elementary school. The majority of the elderly 81.43% had the ability to perform basic activities of daily living and the elderly 52.86% had normal psychological pictures. The worsening of the depressive picture is associated with the worse functioning of elderly who live in HA (p < 0.02). Conclusion: Depression is a factor that is associated with the functional capacity of the institutionalized elderly. The worse the functional level of the institutionalized elderly, the greater the chances of presenting a depressive state.

Telescope performance real-time monitoring based on machine learning

ABSTRACT In astronomy, the demand for high-resolution imaging and high-efficiency observation requires telescopes that are maintained at peak performance. To improve telescope performance, it is useful to conduct real-time monitoring of the telescope status and detailed recordings of the operational data of the telescope. In this paper, we provide a method based on machine learning to monitor the telescope performance in real-time. First, we use picture features and the random forest algorithm to select normal pictures captured by the acquisition camera or science camera. Next, we cut out the source image of the picture and use convolutional neural networks to recognize star shapes. Finally, we monitor the telescope performance based on the relationship between the source image shape and telescope performance. Through this method, we achieve high-performance real-time monitoring with the Large Sky Area Multi-Object Fibre Spectroscopic Telescope, including guiding system performance, focal surface defocus, submirror performance, and active optics system performance. The ultimate performance detection accuracy can reach up to 96.7 per cent.

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography.

PurposeTo develop and compare deep learning (DL) algorithms to detect keratoconus on the basis of corneal topography and validate with visualization methods.MethodsWe retrospectively collected corneal topographies of the study group with clinically manifested keratoconus and the control group with regular astigmatism. All images were divided into training and test datasets. We adopted three convolutional neural network (CNN) models for learning. The test dataset was applied to analyze the performance of the three models. In addition, for better discrimination and understanding, we displayed the pixel-wise discriminative features and class-discriminative heat map of diopter images for visualization.ResultsOverall, 170 keratoconus, 28 subclinical keratoconus and 156 normal topographic pictures were collected. The convergence of accuracy and loss for the training and test datasets after training revealed no overfitting in all three CNN models. The sensitivity and specificity of all CNN models were over 0.90, and the area under the receiver operating characteristic curve reached 0.995 in the ResNet152 model. The pixel-wise discriminative features and the heat map of the prediction layer in the VGG16 model both revealed it focused on the largest gradient difference of topographic maps, which was corresponding to the diagnostic clues of ophthalmologists. The subclinical keratoconus was positively predicted with our model and also correlated with topographic indexes.ConclusionsThe DL models had fair accuracy for keratoconus screening based on corneal topographic images. The visualization mentioned in the current study revealed that the model focused on the appropriate region for diagnosis and rendered clinical explainability of deep learning more acceptable.Translational RelevanceThese high accuracy CNN models can aid ophthalmologists in keratoconus screening with color-coded corneal topography maps.