Accurate Classification Research Articles

Deep ensemble learning for chickenpox detection from clinical images

Chickenpox caused by the varicella-zoster virus is an extremely contagious viral infection common in children and quickly develops into a severe problem. Over 90% of unvaccinated people have been infected. Still, infection occurs at different ages in different parts of the world- Over 70 % of people become infected by the age of 10 years in the United States, the United Kingdom, and Japan, and by the age of 20 in India, West Indies, and South East Asia. Automatic classification of the specific disease is a challenging task to present clinicians to distinguish between different kinds of skin conditions and recommend suitable treatment. Convolutional Neural Networks have recently achieved great success in many machines learning purposes and have presented a state-of-the-art performance in various computer-assisted diagnosis applications. This study proposes a deep neural network-based method that follows an ensemble approach by combining VGG-16, VGG-19, and ResNet-50 architectures to distinguish chickenpox from other skin conditions. Experimental test results have achieved accurate classification with an assuring test accuracy up to 93%.

Classification of gram-positive and gram-negative bacteria using Few-shot learning algorithm

Our research introduces a novel approach to classify bacteria as Gram-positive or Gram-negative using few-shot learning. We employ deep neural networks, specifically Prototypical Networks, to learn distinctive features from bacterial images, enabling accurate classification even with limited data. Experimental results on diverse datasets demonstrate the model's effectiveness and potential for real-world applications in microbiology and healthcare. We also address interpretability, ethics, and data privacy, making it a valuable tool for bacterial classification and diagnostics.

A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM

Cancer poses a significant threat to life due to its aggressive nature, high potential for metastasis, and heterogeneity. Globally, both the men and women are mostly affected by lung and colon (LC) cancer. Early and accurate detection is essential to enhance the well-being of patients facing LC cancer. The traditional techniques face challenges such as low accuracy and high execution time. This work combines the pre-trained deep learning (DL) models like ResNet-50, InceptionV3, and DenseNet with Kernel Extreme Learning Machine (KELM) for the accurate and fast diagnosis of LC cancer using histopathology images. The pre-trained DL models provide a strong foundation for feature extraction, capturing intricate patterns indicative of cancerous tissues. Feature fusion combines the complementary strengths of multiple pre-trained models, enhancing the method's ability to capture diverse features and improve classification accuracy. KELM efficiently handles the high-dimensional feature space generated by the DL models, ensuring fast and accurate classification. Mutation Boosted Dwarf Mongoose Optimization Algorithm (MB-DMOA) further optimizes the model parameters, mitigating the risk of getting stuck in local optima and facilitating better convergence towards the global optimum. By leveraging the advantages of each technique and integrating them into a cohesive framework, the proposed approach achieves superior performance in diagnosing LC cancer, ultimately improving patient outcomes. The proposed study attains accuracy (98.9%), F1-score (97.6%), specificity (96.5%), precision (96.7%) and sensitivity (95.8%). The results illuminate the efficacy of this approach within therapeutic environments.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): −37.8–107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: −68.4–73.4 min) and 32.1 min (95% LoA: −54.4–118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19–2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16–1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Cross-Regional Crop Classification Based on Sentinel-2

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Background. Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in breast imaging reporting and data system (BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM. Materials and methods. A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated. Results. The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2–71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements. Conclusion. The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

AbstractThe early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

High-Order line graphs of fMRI data in major depressive disorder.

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

Applicability and Suitability of the Embryological–Clinical Classification of Female Genital Malformations: A Systematic Review

Complex urogenital malformations are clinically highly relevant; thus, they must be appropriately diagnosed and classified before initiating treatment. Background/Objectives: This study aimed to evaluate the applicability and suitability of the embryological–clinical classification of female genital malformations. Methods: A systematic review of cases of genital malformations reported in the literature from 2000 to 2020 was conducted. Case reports and series with the following combinations: “female genital tract” AND (malformation OR anomaly OR müllerian anomaly OR uterine anomaly OR cervical anomaly OR vaginal anomaly OR cloacal anomaly OR urogenital sinus); and “female genital tract” AND (renal agenesis OR ectopic ureter) were searched. A total of 3124 articles were identified, of which 824 cases of genital malformation were extracted. The characteristics of each malformation were included in a database for further analyses. Results: Using the embryological–clinical classification, 89.9% of the published cases and 86.5% of the 52 cases defined as unclassifiable by their authors have been classified in this review. In 73 cases (72.2%), the classification of the malformation using the AFS system was incomplete because although the type of uterine anomaly of the AFS classification matched that of the embryological–clinical classification, characteristics of the urinary system or the vagina were overlooked when using the AFS system. Following a dispersion matrix, we have been able to show that the embryological–clinical classification system is able to classify and subclassify the genitourinary malformations more accurately. Conclusions: The applicability of the embryological–clinical classification has been confirmed after classifying most of the cases of genital malformation previously published. This system also provides a more complete and accurate classification than other classifying systems exclusively based on Müllerian duct development or uterovaginal parameters, demonstrating its suitability.

Decoding BCOR-ITD Sarcomas: Case Report of a Rare Pediatric Tumor With Challenges in Diagnosis.

Sarcomas characterized by BCOR gene alterations, are a distinct clinico-pathological group of high-grade tumors, that represent 5% of small round cell tumors without EWSR or FUS fusion. Diverse genetic alterations characterize this group, including BCOR-CCNB3 gene fusion being the most common alteration and less frequently internal tandem duplications (ITDs). We present a compelling case of a 3-year-old girl diagnosed with a high-grade nasoethmoidal sarcoma exhibiting BCOR-ITD. The diagnostic process illustrates the histological and immunophenotypic spectrum, requiring an extensive immunohistochemical panel and diverse molecular tests for accurate classification. Additionally, this case highlights the challenges in detecting BCOR-ITDs using different NGS panels, advocating for alternative molecular approaches. Our patient after 10 months since diagnosis is alive with progressive disease. This emphasizes the urgency for ongoing research to refine diagnostic methods and develop effective therapeutic strategies for these rare and aggressive tumors.

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacrylate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10-12 m2, and a range of height of theoretical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt%) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

DATFNets-dynamic adaptive assigned transformer network for fire detection

AbstractFires cause severe damage to the ecological environment and threaten human life and property. Although the traditional convolutional neural network method effectively detects large-area fires, it cannot capture small fires in complex areas through a limited receptive field. At the same time, fires can change at any time due to the influence of wind direction, which challenges fire prevention and control personnel. To solve these problems, a novel dynamic adaptive distribution transformer detection framework is proposed to help firefighters and researchers develop optimal fire management strategies. On the one hand, this framework embeds a context aggregation layer with a masking strategy in the feature extractor to improve the representation of low-level and salient features. The masking strategy can reduce irrelevant information and improve network generalization. On the other hand, designed a dynamic adaptive direction conversion function and sample allocation strategy to fully use adaptive point representation while achieving accurate positioning and classification of fires and screening out representative fire samples in complex backgrounds. In addition, to prevent the network from being limited to the local optimum and discrete points in the sample from causing severe interference to the overall performance, designed a weighted loss function with spatial constraints to optimize the network and penalize the discrete points in the sample. The mAP in the three baseline data sets of FireDets, WildFurgFires, and FireAndSmokes are 0.871, 0.909, and 0.955, respectively. The experimental results are significantly better than other detection methods, which proves that the proposed method has good robustness and detection performance.

Diagnosing the degree of differentiation between types of oral cancer based on extreme deep neural network model and Raman spectroscopy

As a highly prevalent and recurrent cancer, detecting the degree of differentiation in oral cancer is crucial. Current methods rely on biopsies in the presence of significant lesions, which are time-consuming. This study introduces an efficient and rapid approach for mass determination of oral cancer differentiation levels. By leveraging serum Raman spectroscopy combined with deep neural networks and extreme gradient boosting, we performed feature selection and interaction on oral cancer samples of varying differentiation levels, achieving the most accurate and reliable classification of oral cancer differentiation stages.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning

BackgroundCOVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients’ proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.MethodsA case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.ResultsMachine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.ConclusionsThe plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network

Objective. Aiming for the research on the brain–computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples. Approach. In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model. Main results. The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a. Significance. The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.

Squamous Cell Carcinoma of the Bladder: Two Case Report

Squamous cell carcinoma (SCC) of the bladder is a rare variant of bladder cancer, accounting for less than 5% of cases. Its occurrence shows a balanced distribution across sexes, although it tends to affect the Black population disproportionately. Clinical presentation is often nonspecific, with hematuria being the predominant symptom. Diagnosis relies on endoscopic resection of the bladder followed by histopathological examination. Due to its rarity, treatment approaches remain a subject of debate. However, radical cystectomy with extensive lymph node dissection is typically favored. In this study, we present a series comprising 2 cases of squamous cell carcinoma. Our objective is to shed light on the epidemiological, clinical, histopathological, and therapeutic aspects associated with squamous cell carcinoma of the bladder. The two cases presented in this study highlight the importance of accurate diagnosis and classification of squamous cell carcinoma of the bladder for optimal treatment planning and patient outcomes.

Integrating Convolutional Neural Network and Weighted Moving Average for Enhanced Human Fall Detection Performance

This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.