Skin Cancer Detection Using Convolutional Neural Networks
Keywords
Abstract
Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, highlighting the urgent need for early, accurate, and scalable diagnostic methods. This project proposes a deep learning-based solution for automated skin cancer classification using Convolutional Neural Networks (CNNs) trained on the HAM10000 dataset—a benchmark collection of dermatoscopic images representing seven distinct skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. The framework incorporates robust image preprocessing techniques and a customized CNN architecture designed to optimize feature extraction and classification performance across diverse lesion categories. To further enhance model generalization and address potential class imbalance, the project explores data augmentation strategies tailored for medical imagery. A user-friendly interface, developed using Streamlit, enables real-time inference and accessibility for both clinical and non-specialist use. Experimental results demonstrate high classification accuracy and strong differentiation between malignant and benign lesions, supporting the system’s utility as a reliable, cost-effective, and accessible diagnostic aid. This work underscores the significant role of AI-powered tools in augmenting dermatological decision-making, especially in resource-constrained environments where timely diagnosis can substantially impact patient outcomes.
Copyright & License
Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this Article
Tirlangi Indumathi, Dr. G. Narasimha Rao (2025). Skin Cancer Detection Using Convolutional Neural Networks. International Journal of Technology & Emerging Research (IJTER), 1(3), 212-217
BibTeX
@article{ijter2025212507269402,
author = {Tirlangi Indumathi and Dr. G. Narasimha Rao},
title = {Skin Cancer Detection Using Convolutional Neural Networks},
journal = {International Journal of Technology & Emerging Research },
year = {2025},
volume = {1},
number = {3},
pages = {212-217},
issn = {3068-109X},
url = {https://www.ijter.org/article/212507269402/skin-cancer-detection-using-convolutional-neural-networks},
abstract = {Skin cancer remains one of the most prevalent and potentially fatal forms of cancer worldwide, highlighting the urgent need for early, accurate, and scalable diagnostic methods. This project proposes a deep learning-based solution for automated skin cancer classification using Convolutional Neural Networks (CNNs) trained on the HAM10000 dataset—a benchmark collection of dermatoscopic images representing seven distinct skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. The framework incorporates robust image preprocessing techniques and a customized CNN architecture designed to optimize feature extraction and classification performance across diverse lesion categories. To further enhance model generalization and address potential class imbalance, the project explores data augmentation strategies tailored for medical imagery. A user-friendly interface, developed using Streamlit, enables real-time inference and accessibility for both clinical and non-specialist use. Experimental results demonstrate high classification accuracy and strong differentiation between malignant and benign lesions, supporting the system’s utility as a reliable, cost-effective, and accessible diagnostic aid. This work underscores the significant role of AI-powered tools in augmenting dermatological decision-making, especially in resource-constrained environments where timely diagnosis can substantially impact patient outcomes.},
keywords = {Skin Cancer Detection, Deep Learning, Convolutional Neural Networks (CNN), HAM10000, Image Classification, Melanoma, Dermatoscopic Images, Medical Image Analysis, Data Augmentation, Streamlit Interface, Automated Diagnosis, Artificial Intelligence in Healthcare, Early Detection, Lesion Classification, Medical AI Tools},
month = {Jul},
}