A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.

The identification and early treatment of retinal disease can help to prevent loss of vision. Early diagnosis allows a greater range of treatment options and results in better outcomes. Optical coherence tomography (OCT) is a technology used by ophthalmologists to detect and diagnose certain eye conditions.

An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.

This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are considered as labeled and an unlabeled dataset. The unlabeled dataset is split into three subsets which are assumed to be collected from three hospitals.

HGATT_LR: transforming review text classification with hypergraphs attention layer and logistic regression.

Text classification plays a major role in research such as sentiment analysis, opinion mining, and customer feedback analysis. Text classification using hypergraph algorithms is effective in capturing the intricate relationships between words and phrases in documents. The method entails text preprocessing, keyword extraction, feature selection, text classification, and performance metric evaluation.

Development of an automated optimal distance feature-based decision system for diagnosing knee osteoarthritis using segmented X-ray images.

Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement.

Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation.

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis.

Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model.

Introduction: Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.

Purpose: The study aims to classify breast ultrasound images into benign and malignant tumors.

A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language.

Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER.

An Automated Broncho-Arterial (BA) Pair Segmentation Process and Assessment of BA Ratios in Children with Bronchiectasis Using Lung HRCT Scans: A Pilot Study.

Bronchiectasis in children can progress to a severe lung condition if not diagnosed and treated early. The radiological diagnostic criteria for the diagnosis of bronchiectasis is an increased broncho-arterial (BA) ratio. From high-resolution computed tomography (HRCT) scans, the BA pairs must be detected first to derive the BA ratio.

High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images.

In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis.

Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases.

Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect.

An Effective Ensemble Machine Learning Approach to Classify Breast Cancer Based on Feature Selection and Lesion Segmentation Using Preprocessed Mammograms.

Background: Breast cancer, behind skin cancer, is the second most frequent malignancy among women, initiated by an unregulated cell division in breast tissues. Although early mammogram screening and treatment result in decreased mortality, differentiating cancer cells from surrounding tissues are often fallible, resulting in fallacious diagnosis. Method: The mammography dataset is used to categorize breast cancer into four classes with low computational complexity, introducing a feature extraction-based approach with machine learning (ML) algorithms.

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system.

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique.

Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets.

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions.

Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method.

LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images.

In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray imaging using a fine-tuned CNN model is proposed.

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images.

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus.

Contraceptive access, choices, and discontinuation among the urban users in Karachi, Pakistan: Findings from a comparative analysis of Pakistan demographic and health survey 2012-12 and 2017-18.

Objective: To explore and assess the contraceptive access, choices, and discontinuation among the urban users in Karachi using the last two Demographic and Health Surveys in Pakistan.

Methods: A comparative analysis of the six districts of Karachi (Urban only) using Pakistan Demographic and Health Survey 2012-13 (sample size 2324) and 2017-18 (sample size 2896) of the currently married women of reproductive age 15-49 years was designed and conducted. For the current study, we used descriptive statistics on contraceptive use, method-mix, unmet need for family planning, method-specific discontinuation, sources of modern contraceptive use by channel (public and private), and exposure to family planning messaging.

Tianeptine Abuse Leading to an Episode of Psychosis: A Case Report and Literature Review.

Tianeptine is an atypical mu-opioid receptor agonist. It is available as an antidepressant outside the United States, but it is also classified as a controlled substance in many other countries. It is not approved by the United States Food and Drug Administration for the treatment of depression but it can be obtained online without a prescription.

Maternal hypothalamic-pituitary-adrenal axis response to foraging uncertainty: A model of individual vs. social allostasis and the "Superorganism Hypothesis".

Introduction: Food insecurity is a major global contributor to developmental origins of adult disease. The allostatic load of maternal food uncertainty from variable foraging demand (VFD) activates corticotropin-releasing factor (CRF) without eliciting hypothalamic-pituitary-adrenal (HPA) activation measured on a group level. Individual homeostatic adaptations of the HPA axis may subserve second-order homeostasis, a process we provisionally term "social allostasis.

Neurobiology of Maternal Stress: Role of Social Rank and Central Oxytocin in Hypothalamic-Pituitary Adrenal Axis Modulation.

Background: Chronic stress may conceivably require plasticity of maternal physiology and behavior to cope with the conflicting primary demands of infant rearing and foraging for food. In addition, social rank may play a pivotal role in mandating divergent homeostatic adaptations in cohesive social groups. We examined cerebrospinal fluid (CSF) oxytocin (OT) levels and hypothalamic-pituitary adrenal (HPA) axis regulation in the context of maternal social stress and assessed the contribution of social rank to dyadic distance as reflective of distraction from normative maternal-infant interaction.

Rationale, design, and cohort enrolment of a prospective observational study of the clinical performance of the new contraceptive implant (Femplant) in Pakistan.

Introduction: The use of hormonal implants has gained positive traction in family planning programs in recent times. Compared to other popular methods, such as long-term reversible intrauterine devices, the use of hormonal implants as a family planning method has distinct advantages in terms of long-term efficiency and better user compliance and availability. This paper presents a study protocol to document and evaluate the efficacy, safety, and acceptability of Femplant (contraceptive implant) in Pakistan during the first year of its use among married women of reproductive age (18-44 years) at clinics in two provinces of Pakistan (Sindh and Punjab).