Machine Learning Mastery: Leveraging Convolutional Neural Networks to Classify Skin Cancers as Benign or Malignant in the ISIC Database - Chitralekha Dwivedi, Ritu Patidar, Sanjay Patidar, Upendra Singh, Krupa Purohit (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This research tackles the urgent need for enhanced precision in the detection of skin cancer, a common yet potentially deadly disease. Traditional diagnostic techniques frequently fall short in accuracy, prompting unnecessary and invasive medical interventions. Previous attempts to employ machine learning for distinguishing among different types of skin cancer have not been fully successful in achieving effective differentiation. To address these challenges, the study proposes an innovative approach utilizing Convolutional Neural Networks (CNN) for the autonomous identification of skin cancer. The designed CNN architecture incorporates three hidden layers, with the number of channels in each layer progressively increasing from 16 to 32, and then to 64. The model leverages the AdamW optimization algorithm with a learning rate set at 0.001, a choice that has proven to be highly effective. In evaluations conducted using the International Skin Imaging Collaboration (ISIC) dataset, which involved classifying skin lesions as either benign or malignant, the proposed CNN methodology demonstrated a remarkable accuracy rate of 96%. This level of precision indicates a significant advancement in the field of skin cancer diagnostics, highlighting the potential of CNN-based models to revolutionize the early detection and treatment of this condition.