An Interpretable Skin Cancer Classification Using Optimized Convolutional Neural Network for a Smart Healthcare

Abstract

Skin cancer is a prevalent and life-threatening disease worldwide, making early and accurate diagnosis crucial for effective treatment. In the era of smart healthcare, the integration of artificial intelligence and computer-aided diagnosis systems has shown promise in improving the diagnostic accuracy and interpretability of skin cancer classification. This project presents an innovative approach for the interpretable classification of skin cancer using an optimized Convolutional Neural Network (CNN). The proposed system leverages deep learning techniques to analyze dermatoscopic images, offering a non-invasive and efficient solution for early skin cancer detection. Through careful optimization of the CNN architecture, feature extraction, and training parameters, our model achieves enhanced classification performance while maintaining a high degree of interpretability. Furthermore, the system incorporates advanced visualization and explanation techniques to provide clinicians and patients with transparent insights into the decision-making process of the CNN. This interpretability is crucial for building trust in the automated diagnostic system and facilitating effective collaboration between AI and healthcare professionals. The project's results demonstrate the potential for improved skin cancer diagnosis through a transparent and optimized CNN model, contributing to the advancement of smart healthcare. The combination of AI technology and interpretability not only enhances the accuracy of skin cancer classification but also ensures that the decision-making process is comprehensible and actionable for medical practitioners, ultimately leading to better patient outcomes. Key Words: Skin cancer, Healthcare, Artificial Intelligence, Deep Learning, OpenCV, RNN