Abstract: Artificial Intelligence (AI), which involves the simulation of human intelligence processes by machines, is increasingly being applied across various domains, with one of its most impactful uses in the medical field. It is revolutionizing healthcare by enabling faster, more accurate diagnoses and improving patient outcomes through data-driven decision-making. The proposed system presents an AI-based automated diagnostic framework for the early detection of melanoma (skin cancer) and diabetes—two of the most prevalent and critical health conditions. Leveraging deep learning and image processing techniques, the system enhances diagnostic precision and efficiency. Convolutional Neural Networks (CNNs), a core AI method in medical imaging, are utilized for image-based disease classification. For melanoma detection, dermoscopic images are analyzed using pre-trained CNN models to identify cancerous patterns. For diabetes, retinal image analysis is integrated with clinical parameters to assess disease risk. This AI-powered system automates feature extraction, reduces the need for human intervention, and provides real-time, accurate diagnostic results. Developed using MATLAB, the framework shows high classification accuracy and robustness under varying image conditions. This project underscores the expanding role of AI in healthcare and aims to make intelligent diagnostics more accessible to medical professionals. Future work will focus on expanding datasets, enhancing model generalization, and integrating additional clinical features for more comprehensive health assessments.
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DOI:
10.17148/IJARCCE.2025.14535
