MAHATVAK CHIKITSA- AUTOMATED SKIN ONCOLOGY DIAGNOSIS WITH CLINICAL DECISION SUPPORT

Abstract: Skin cancer is one of the major dermatological conditions where early identification of suspicious skin lesions can assist in timely clinical assessment. However, distinguishing different lesion types from dermoscopic images is challenging because several categories may exhibit similar visual characteristics. This project presents a deep learning- based approach for automated skin lesion classification using dermoscopic images obtained from the International Skin Imaging Collaboration (ISIC) dataset. The proposed system performs image preprocessing, resizing, normalization, augmentation, and class balancing to prepare the dataset for model development. Five transfer-learning architectures, namely VGG19, VGG16, ResNet50, MobileNet, and EfficientNet, are trained and evaluated for the classification of nine skin lesion categories. The models are compared using classification performance measures including accuracy, precision, recall, and F1-score. Among the evaluated architectures, EfficientNet achieves the highest performance with a test accuracy of 90.11% and a macro F1-score of 90.57%. The selected model is further integrated into a Flask-based web application that allows users to upload a dermoscopic image and receive the predicted lesion category, confidence level, and preliminary guidance. The developed system is intended to support computer-aided screening and decision-making, while final diagnosis and treatment decisions remain with qualified healthcare professionals.

Keywords: Skin Cancer Detection, Skin Lesion Classification, Deep Learning, Transfer Learning, ISIC Dataset, Dermoscopic Images, EfficientNet, Flask.