Kidney disease is a social and global health concern where early detection is crucial to reducing mortality and improving treatment outcomes. Traditional diagnostic methods are time-consuming and prone to human error. To address the issue, this study proposes an efficient automated deep learning diagnostic system using medical imaging for kidney disease detection and classification. The framework integrates DenseNet121 and EfficientNetB0 for deep feature extraction, followed by SVM, Random Forest, and XGBoost classifiers combined via soft voting. The proposed system was evaluated on 12,446 CT images encompassing four kidney classes: cyst, stone, tumor, and normal. The proposed model achieved outstanding performance metrics with an accuracy of 99.24% and an F1-score of 99%. The proposed model enables early and accurate detection of kidney disease, aiding timely treatment, especially in resource-limited settings.

Oral cancer (OC) remains a major global health concern with survival often limited by late diagnosis. Early and accurate detection is essential to improve patient outcomes and guide treatment decisions. In this study we propose a computer aided diagnostic (CAD) framework for classifying oral squamous cell carcinoma from histopathology images. The model combines Swin transformer for hierarchical feature extraction with vision transformer (ViT) to capture long range dependencies across image regions. SHapley Additive exPlanations (SHAP) based feature selection enhances interpretability by highlighting the most informative features while preprocessing steps such as stain normalization and contrast enhancement improve model generalization and reduce sample variability. Evaluated on a publicly available dataset the framework achieved 99.25% accuracy (ACC) 99.21% sensitivity and a matthews correlation coefficient (MCC) of 98.21% outperforming existing methods. Ablation studies highlighted the importance of positional encoding and statistical analyses confirmed the robustness and reliability of results. To support real-time inference and scalable deployment the proposed model has been integrated into a FastAPI-based web application. This framework offers a powerful interpretable and practical tool for early OC detection and has potential for integration into routine clinical workflows.

Diabetes is a significant global health issue that demands accurate, accessible, and non-invasive diagnostic methods for effective prevention and treatment. Conventional diagnostic systems are often expensive, painful, time-consuming, and not universally available. In this study, we present a smart system for acetone estimation using simulated breath and a recurrent neural network (RNN) model. The detection system employs a new sensor fabricated from a composite of 1D nanostructured KWO (K₂W₇O₂₂) and 2D nanosheet MXene (Ti₃C₂), designed to measure the chemiresistive response to acetone by mimicking human breath. Resistance data collected by the sensor are used to compute sensitivity values for each acetone concentration (in parts per million, PPM). These values serve as input features for the RNN model, which learns to evaluate health as healthy, high-risk, or diabetic. Trained on acetone concentrations ranging from 0.4 to 2 PPM, the RNN achieves an R² of 99.41% in predicting potential for accurate breath acetone prediction. In future work, we aim to develop a smart device and mobile application based on this model to facilitate real-time diabetes monitoring and prediction.

The automated and accurate results of classifying histopathology images are necessary in the early detection of cancer, especially the common cancers such as Colorectal Cancer (CRC) and Lung Cancer (LC). Nonetheless, classical deep learning frameworks often face challenges because the intra-class variations are large, the relations across classes are alike, and the quality of images is not stable. In order to eliminate these constraints, a multi-layer diagnostic framework is offered in detail. This process starts with a strong preprocessing pipeline, which involves gamma correction, bilateral filtering, and adaptive CLAHE, resulting in statistically significant changes in image quality quantitative measures. The hybrid attention architecture is presented and includes an Xception backbone, a Convolutional Block Attention Module (CBAM), a Transformer block, and an MLP classifier to successfully combine local features with global context. The proposed model achieved an outstanding performance with a classification of 99.98%, 99.58%, and 99.33% percent on LC25000, CRC-VAL-HE-7K, and NCT-CRC-HE-100K when tested on three publicly available datasets. In order to enhance transparency, very detailed explainability analyses are conducted with the help of layer-wise feature visualization and Grad-CAM. Finally, the real-world example of this framework is presented by its implementation in a web-based platform, which can be a useful and easy-to-use tool in helping to diagnose a pathology.

The precise and automated classification of histopathology images is essential for early detection of cancer, particularly for widespread cancers like Colorectal Cancer (CRC) and Lung Cancer (LC). However, traditional deep learning models frequently encounter challenges due to significant intra-class variability, similarities between different classes, and inconsistent image quality. To overcome these limitations, a detailed multi-layer diagnostic framework is proposed. This method begins with a robust preprocessing pipeline that includes gamma correction, bilateral filtering, and adaptive CLAHE, leading to substantial improvements in quantitative metrics of image quality. A deep learning architecture based on hybrid attention mechanisms has been introduced, which integrates an Xception backbone, a Convolutional Block Attention Module (CBAM), a Transformer block, and an MLP classifier to effectively merge local features with global context. When evaluated on three publicly accessible datasets, the proposed model attained exemplary results, reaching classification accuracies of 99.98% on LC-2500, 99.58% on CRC-VAL-HE-7K, and 99.29% on NCT-CRC-HE-100K. To improve transparency, thorough explain ability analyses are performed utilizing layer-wise feature visualization and Grad-CAM. Lastly, the practical application of this framework is showcased through its implementation on a web-based platform, offering a valuable and user-friendly tool to assist in pathological diagnosis.