Deep Learning–Based Skin Cancer Detection Using VGG and Inception-V3 with a  Web-Based Diagnostic System

Karankumar Waghmare; Mitali Ingle

doi:10.63363/aijfr.2026.v07i02.5173

Deep Learning–Based Skin Cancer Detection Using VGG and Inception-V3 with a Web-Based Diagnostic System

Author(s)	Karankumar Waghmare, Prof. Mitali Ingle
Country	India
Abstract	Skin cancer is one of the most common and dangerous diseases around the world, and catching it early is very important for helping people survive and making treatment easier. This study introduces a skin cancer detection and diagnosis system that uses deep learning and works online to help identify skin lesions in their early stages. The method uses convolutional neural networks with an Inception structure and transfer learning to automatically find key visual features from images of skin lesions and sort them into malignant or benign categories. The system uses publicly available datasets like ISIC and HAM10000 for training and testing, and data preprocessing and enhancement are used to make the model more reliable. Training the model is done using cloud-based GPU resources to make the process faster and more scalable. The trained model is part of a secure web app that allows users to log in, upload images, see predictions visually, manage their history of results, and get advice on what to do next. The goal of the system is to connect advanced medical diagnosis with easy access to healthcare by offering a dependable, user-friendly, and scalable tool for initial skin cancer screening
Keywords	Skin Cancer Detection, Deep Learning, Convolutional Neural Networks, Inception Models, Transfer Learning, Medical Image Analysis, Web-Based Diagnostic Systems, Artificial Intelligence in Healthcare
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-24
DOI	https://doi.org/10.63363/aijfr.2026.v07i02.5173

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Deep Learning–Based Skin Cancer Detection Using VGG and Inception-V3 with a Web-Based Diagnostic System

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