Automated Skin Cancer Detection: Leveraging Hyperband for Optimized Convolutional Neural Networks

Abstract

Skin cancer is an extremely common disease around the world and proactive identification is key to raising survival rates. The area of study of skin cancer detection offers a compelling use case for the application of artificial intelligence (AI) within the domain of image-based diagnosis.. Through the analysis of large datasets, AI algorithms have the capacity to classify clinical images with remarkable accuracy. Convolutional Neural Networks (CNNs) have proven to be effective as dermatologists in diagnosing skin lesions. However, conventional hyperparameter tuning techniques like manual selection or grid search are computationally intensive and time consuming. This work utilizes Hyperband which is an adaptive optimization algorithm to optimize significant parameters like learning rate, dropout rate, and batch size efficiently on datasets like HAM10000, ISIC Archive, and Kaggle's Skin Cancer dataset. The proposed framework of Hyperband optimization has resulted in an increased accuracy of 92.8% in terms of skin cancer detection. Hyperband-optimized CNNs outperformed baseline models by dynamically allocating resources and removing poor configurations in a quick time. The work presents the hyperband model with improved accuracy, precision, recall, F1-score, and AUC-ROC scores and significantly lower computational overhead. These results prove the revolutionizing potential of automated hyperparameter optimization platforms in enhancing AI-assisted medical diagnosis, and providing scalable and highly efficient solutions for early skin cancer detection.