• Soniya Mery Jacob
• Amrita J
• Sonu Saji Varghese
• Jishnu K Satheesh
• Rani Saritha R

The accurate diagnosis and classification of skin lesions are critical for the early detection and treatment of dermatological diseases. However, this task is often challenging due to the wide variety of skin conditions and the limited availability of medical resources. To address this issue, we propose ‘SKINSCAN’ a skin lesion classification system that leverages the HAM10000 dataset, a comprehensive and diverse collection of dermatoscopic images. Our system combines state-of-the-art deep learning techniques, specifically Convolutional Neural Networks (CNNs), with traditional machine learning methods, including image classification. The approach enables the accurate detection and classification of skin lesions. The HAM10000 dataset, comprising approximately 10,000 high-resolution images, provides a realistic and diverse set of cases for training and evaluation. Our system not only detects skin diseases but also offers lesion classification, aiding in the differentiation of benign and malignant conditions. Through rigorous experimentation and evaluation, we demonstrate the efficacy of our system in terms of accuracy and reliability. By making this technology available through a user-friendly web application, we aim to bridge the gap in medical infrastructure and facilities, enabling users to access skin lesion diagnosis and classification from the comfort of their homes. This system holds the potential to revolutionize dermatological healthcare by enhancing early disease detection and improving patient outcomes. It offers a promising solution to the challenges of skin lesion diagnosis and classification, contributing to better healthcare access and outcomes for individuals worldwide.

Skin lesion detection, Convolutional Neural Networks, medical image analysis, deep learning, image classification, dermatology

"SKINSCAN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.i358-i361, March-2024, Available :http://www.jetir.org/papers/JETIR2403846.pdf

"SKINSCAN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppi358-i361, March-2024, Available at : http://www.jetir.org/papers/JETIR2403846.pdf