Paper Contents
Abstract
Skin conditions are one of the most common health ailments worldwide, frequently causing serious complications or death if not diagnosed in early stages. Standard dermatological diagnosis requires significant reliance on the skill of expert readers and advanced equipment, both of which are frequently unavailable in rural and low-resource settings. This paper puts forward an intelligent Skin Disease Detection System utilizing Convolutional Neural Networks (CNNs) and Transfer Learning methods to identify dermoscopic images into groups like melanoma, eczema, and healthy skin conditions. Pre-trained deep learning models such as InceptionV3, ResNet50, and EfficientNet have been fine-tuned in order to attain strong feature extraction and high prediction accuracy. The framework proposed preprocesses input images by resizing and normalization, uses CNN-based feature learning, and classifies lesions with a Softmax activation layer. Experimental results show that the use of transfer learning greatly improves diagnostic performance despite the paucity of medical datasets. The model developed has high accuracy and shortened inference time, which makes it applicable for real-time deployment in a clinical setting via web or mobile portals. This method facilitates early diagnosis, minimizes human diagnostic mistakes, and encourages affordable and available dermatological service through artificial intelligence.
Copyright
Copyright © 2025 Sakshi Devendra Chaudhary, Rohan Janardhan Bhandare, Sarthak Gorakshnath Bhoknal, Prasad Vijay Dandgavhal, Chanchal B. Kakad. This is an open access article distributed under the Creative Commons Attribution License.
