Skin cancer constitutes a significant global health issue, and early detection is crucial for enhancing treatment efficacy and patient survival rates. This study presents a sophisticated system for the early identification of skin cancer utilizing computer vision and machine learning techniques. The proposed framework analyzes dermoscopic images through a systematic pipeline comprising preprocessing, contrast enhancement, RGB normalization, and feature extraction. This enhances image uniformity and augments the model's efficacy. A convolutional neural network architecture is developed to autonomously categorize images of skin lesions into seven clinically significant diagnostic categories. The model employs cost-sensitive learning techniques to enhance the recognition of minority classes while maintaining overall accuracy. This addresses the prevalent issue of class imbalance in medical image datasets. 5-fold cross-validation was employed to rigorously evaluate the model's performance, ensuring its efficacy across a diverse array of samples. The system emphasizes usability and accessibility alongside predictive performance. The platform features a user-friendly web interface that enables users to upload images and receive diagnostic insights promptly. The design prioritizes patient privacy, data security, and ethical considerations due to the highly confidential nature of medical information. The proposed methodology aims to aid clinicians and individuals in early screening and decision-support processes by integrating automated analysis and straightforward implementation. This study demonstrates how deep learning-based diagnostic tools can effectively and extensively classify dermoscopic images. This will enhance early detection strategies and improve the management of skin cancer.
Tajanpure et al. (Sun,) studied this question.