Paper Title
Bayesian Hyperparameter Optimization for Custom Convolutional Neural Networks in Skin Cancer Recognition

Skin cancer is a significant public health concern, and early detection is crucial for effective treatment. Deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have shown promise in automating skin cancer recognition from medical images. However, designing an optimal CNN architecture with the right hyperparameters remains a challenging and time-consuming task. This article presents an innovative approach to address this challenge using Bayesian Hyperparameter Optimization. Instead of relying on pre-trained models, we explore the creation of custom CNN architectures tailored specifically for skin cancer recognition. Bayesian optimization is employed to systematically search and discover the optimal combination of hyperparameters, significantly enhancing model performance. We discuss the development of a custom CNN, data preprocessing techniques, and the incorporation of Bayesian optimization to fine-tune hyperparameters. Our experiments demonstrate the effectiveness of this approach, resulting in a skin cancer recognition model with superior accuracy compared to conventional methods. Through this work, we provide a valuable contribution to the field of dermatology by showcasing the potential of custom CNNs and Bayesian optimization in automating skin cancer diagnosis. Our findings open up new avenues for improving the accuracy and efficiency of skin cancer recognition systems, with broader implications for the field of medical image analysis. Keywords - skin cancer, Image processing, convolutional neural networks, Bayesian optimization