International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A CNN Based Automated System for Early Skin Cancer Detection |
|---|---|
| ABSTRACT | In today's healthcare system, finding skin cancer early is very important for getting better treatment results and lowering risks. The Skin Cancer Detection System is a cutting-edge program that uses deep learning and artificial intelligence (AI) to improve the way diseases are found. It was made with Python and Flask, and it uses the MobileNetV2 model to process and analyze dermoscopic skin images. The system gives useful outputs like prediction results, a percentage of confidence, and health advice on what to do next. The app has a React interface that is both interactive and easy to use, which makes for a smooth and efficient user experience. The solution is very useful because it has features like scan history, doctor recommendations, and the ability to make appointments. Overall, this system is a useful and scalable tool that helps people take precautions early by using modern AI technology to meet the needs of real-world healthcare. |
| AUTHOR | RENISH M, SANJAI ASHOK K, SHELTON J, P.JENIFER UG Student, Dept. of CSE, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India Assistant Professor, Dept. of CSE, Francis Xavier Engineering College, Tirunelveli, Tamil Nadu, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404040 |
| pdf/40_A CNN Based Automated System for Early Skin Cancer Detection.pdf | |
| KEYWORDS | |
| References | 1. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016. 2. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” 2018. 3. I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,” MIT Press, 2016. 4. A. Esteva et al., “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature, 2017. 5. T. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, 2017. 6. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” 2015. 7. O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, 2015. 8. D. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column Deep Neural Networks for Image Classification,” 2012. 9. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” 2017. 10. World Health Organization, “Skin Cancer Prevention and Early Detection,” 2020. |
