metungtech.comStroke is one of the most prevalent causes of death and disability in the world, although it is preventable and treated. Improving clinical outcomes and lowering the burden of disease are significantly aided by early stroke detection and prompt treatments. Because machine learning techniques can be used to detect strokes, they have garnered a lot of attention in recent years. Finding trustworthy techniques, algorithms, and characteristics that support healthcare providers in making well-informed decisions on stroke prevention and treatment is the goal of this project. In order to accomplish this, we have created an early stroke detection system that uses brain CT scans in conjunction with the CNN algorithm to identify strokes at an extremely early stage. These CT image characteristics are incorporated into the CNN model. The diagnostic system's accuracy, precision, recall, F1 score, Receiver Operating Characteristic Cu

Knee osteoarthritis (KOA) is a progressive joint disorder often diagnosed via X-ray imaging due to its accessibility and low cost. The severity is typically assessed using the Kellgren and Lawrence (KL) grading scale. Early detection using this method can slow disease progression through timely intervention. In this study, four datasets were combined and augmented using Deep Convolutional Generative Adversarial Networks (DCGAN), resulting in 110,232 enhanced images. Advanced preprocessing techniques such as adaptive histogram equalization (AHE), fast non- local means filtering, and resizing were applied to improve image quality. A modified Compact Convolutional Transformer model, KOA-CCTNet, was developed and fine-tuned by optimizing multiple hyperparameters to efficiently handle large-scale data and reduce training time. Experimental results showed KOA-CCTNet outperformed state-of-the-art models including ResNet50 (80.77%), MobileNetv2 (79.98%), DenseNet201 (80.23