Diabetic retinopathy is a serious ocular disorder resulting from diabetes, characterized by damage to the retinal blood vessels, which may result in visual impairment and blindness if left untreated. To address the constraints of current approaches, such as dataset imbalance, diversity, and generalization difficulties, this study introduces VR-FuseNet, a novel hybrid deep learning model for automated diabetic retinopathy (DR) detection. VR-FuseNet integrates the advantages of two advanced convolutional neural networks: VGG19, which excels in capturing intricate spatial characteristics, and ResNet50V2, recognized for its profound hierarchical feature extraction capabilities. A hybrid dataset composed of five publicly accessible diabetic retinopathy datasets (APTOS 2019, DDR, IDRiD, Messidor 2, and Retino) is employed, incorporating critical preprocessing methods including the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing and Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement to enhance robustness and generalizability. The proposed VR-FuseNet model attains exceptional diagnostic performance with > 91% accuracy, exceeding > 92% in precision, recall, and F1-score, and achieving > 98% AUC, surpassing individual constructs across all performance parameters. To improve clinical utility and interpretability, various Explainable Artificial Intelligence (XAI) techniques, including gradient-based methods, are employed to produce visual explanations that distinctly highlight retinal features influencing the model’s predictions, thereby allowing clinicians to interpret and validate diagnostic decisions.

Named Entity Recognition (NER) in regional dialects is a critical yet underexplored area in Natural Language Processing (NLP), especially for low-resource languages like Bangla. While NER systems for Standard Bangla have made progress, no existing resources or models specifically address the challenge of regional dialects such as Barishal, Chittagong, Mymensingh, Noakhali, and Sylhet, which exhibit unique linguistic features that existing models fail to handle effectively. To fill this gap, we introduce ANCHOLIK-NER, the first benchmark dataset for NER in Bangla regional dialects, comprising 17,405 sentences and 101,817 words annotated with 10 entity tags across 5 regions. The dataset was sourced from publicly available resources and supplemented with manual translations, ensuring alignment of named entities across dialects. We evaluate three transformer- based models—Bangla BERT, Bangla Bert Base, and BERT Base Multilingual Cased—on this dataset. Bangla BERT achieved the highest performance overall, with F1-scores of 82.27% (Mymensingh), 81.48% (Barishal), 78.75% (Sylhet), 78.50% (Noakhali), and 75.31% (Chittagong). These results highlight strong recognition capability in Mymensingh and Barishal, while dialectal variation in Chittagong remains challenging. As no prior NER resources exist for Bangla regional dialects, this work provides a foundational dataset and baseline benchmarks to facilitate future research. Future work will focus on dialect-aware model adaptation and expanding coverage to additional regions.

Advanced diagnostic instruments are crucial for the accurate detection and treatment of lung diseases, which affect millions of individuals globally. This study examines the effectiveness of deep learning and transfer learning models using a hybrid dataset, created by merging four individual datasets from Bangladesh and global sources. The hybrid dataset significantly enhances model accuracy and generalizability, particularly in detecting COVID-19, pneumonia, lung opacity, and normal lung conditions from chest X-ray images. A range of models, including CNN, VGG16, VGG19, InceptionV3, Xception, ResNet50V2, InceptionResNetV2, MobileNetV2, and DenseNet121, were applied to both individual and hybrid datasets. The results showed superior performance on the hybrid dataset, with VGG16, Xception, ResNet50V2, and DenseNet121 each achieving an accuracy of 99%. This consistent performance across the hybrid dataset highlights the robustness of these models in handling diverse data while maintaining high accuracy. To understand the models implicit behavior, explainable AI techniques were employed to illuminate their black-box nature. Specifically, LIME was used to enhance the interpretability of model predictions, especially in cases of misclassification, contributing to the development of reliable and interpretable AI-driven solutions for medical imaging.

The brain is a highly complex organ that manages many important tasks, including movement, memory and thinking. Brain-related conditions, like tumors and degenerative disorders, can be hard to diagnose and treat. Magnetic Resonance Imaging (MRI) serves as a key tool for identifying these conditions, offering high-resolution images of brain structures. Despite this, interpreting MRI scans can be complicated. This study tackles this challenge by conducting a comparative analysis of Vision Transformer (ViT) and Transfer Learning (TL) models such as VGG16, VGG19, Resnet50V2, MobilenetV2 for classifying brain diseases using MRI data from Bangladesh based dataset. ViT, known for their ability to capture global relationships in images, are particularly effective for medical imaging tasks. Transfer learning helps to mitigate data constraints by fine-tuning pre-trained models. Furthermore, Explainable AI (XAI) methods such as GradCAM, GradCAM++, LayerCAM, ScoreCAM, and Faster-ScoreCAM are employed to interpret model predictions. The results demonstrate that ViT surpasses transfer learning models, achieving a classification accuracy of 94.39%. The integration of XAI methods enhances model transparency, offering crucial insights to aid medical professionals in diagnosing brain diseases with greater precision.

Leaf diseases are harmful conditions that affect the health, appearance and productivity of plants, leading to significant plant loss and negatively impacting farmers' livelihoods. These diseases cause visible symptoms such as lesions, color changes, and texture variations, making it difficult for farmers to manage plant health, especially in large or remote farms where expert knowledge is limited. The main motivation of this study is to provide an efficient and accessible solution for identifying plant leaf diseases in Bangladesh, where agriculture plays a critical role in food security. The objective of our research is to classify 21 distinct leaf diseases across six plants using deep learning models, improving disease detection accuracy while reducing the need for expert involvement. Deep Learning (DL) techniques, including CNN and Transfer Learning (TL) models like VGG16, VGG19, MobileNetV2, InceptionV3, ResNet50V2 and Xception are used. VGG19 and Xception achieve the highest accuracies, with 98.90% and 98.66% respectively. Additionally, Explainable AI (XAI) techniques such as GradCAM, GradCAM++, LayerCAM, ScoreCAM and FasterScoreCAM are used to enhance transparency by highlighting the regions of the models focused on during disease classification. This transparency ensures that farmers can understand the model's predictions and take necessary action. This approach not only improves disease management but also supports farmers in making informed decisions, leading to better plant protection and increased agricultural productivity.

Convolutional Neural Networks (CNNs) serve as a foundational component in the domain of Computer Vision (CV). In order to enhance the Interpretability of CNN models, a critical aspect for clinical adoption, this study incorporates Explainable AI (XAI) methodologies. Through applying CNNs and XAI to a dataset comprising 5285 Brain MRI Images, a classification accuracy of 86% was achieved. The LIME framework was employed to generate localized explanations, thereby augmenting the model's transparency and facilitating a deeper understanding of its decision-making process. This research explores the potential of synergistically integrating deep learning and XAI to foster the development of more reliable and comprehensible medical image analysis systems. Such systems hold the promise of improving diagnostic accuracy and clinical decision-making by providing healthcare professionals with transparent and explainable insights into the model's predictions, ultimately leading to more informed and effective patient care.