Skin lesion classification for Melanoma Detection using deep learning

Author(s)	Avi Shah, Prof. Ronak Chauhan
Country	India
Abstract	Melanoma is a highly aggressive skin cancer where early and accurate diagnosis is critical for improving patient survival. Traditional diagnostic methods rely on visual assessment and are often subjective and inconsistent, creating a need for reliable automated solutions. This study proposes a deep learning-based skin lesion classification system using Convolutional Neural Networks (CNNs) trained on dermoscopic images. The model automatically learns discriminative features, reducing dependence on manual feature extraction and minimizing human bias. To enhance performance and generalization, preprocessing techniques such as normalization, data augmentation (flipping, rotation, scaling), and noise reduction are applied. The system is evaluated using accuracy, precision, recall, and F1-score, demonstrating strong diagnostic performance in melanoma detection. Overall, the study highlights the potential of deep learning to support clinical decision-making and improve early diagnosis, leading to better patient outcomes.
Keywords	Melanoma Detection, Deep Learning, Convolutional Neural Networks (CNN), Skin Lesion Classification, Dermoscopic Image Analysis.
Field	Engineering
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-03-31

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology