Optimizing Convolutional Neural Network Impact of Hyperparameter Tuning and Transfer Learning

Abstract

This chapter examines skin cancer, particularly melanoma, which has a high mortality rate, making early diagnosis essential. It explores how convolutional neural networks (CNNs) can improve melanoma detection, providing a detailed technical analysis of hyperparameters and their impact on model performance. Strategies for tuning hyperparameters, including random search and Bayesian optimization, are demonstrated. Using the HAM10000 dataset, the chapter assesses the impact of different hyperparameter settings on accuracy, sensitivity, and specificity. Issues like class imbalance are addressed with data augmentation and resampling. The optimization methods improve DenseNet121 and MobileNetV2 accuracies to 85.65% and 84.08%, respectively.