Melanoma classification with neural networks using an unbalanced dataset of skin lesion images - KLAUTAU, Sofia Pinheiro (2025)
The applications of Artificial Intelligence (AI) in various fields are extensive and have the potential to revolutionize various aspects of modern healthcare, for example, demonstrating promising advances in improving the accuracy and efficiency of skin cancer detection and classification. This area of study is of significant importance as it seeks to improve early identification and diagnosis of skin cancer, positively impacting patient outcomes and treatment strategies. This work describes a study carried out on the use of unbalanced datasets for the classification of images of skin cancer lesions using Artificial Neural Networks, more specifically, a dataset that has over 98% of samples belonging to the negative class. Three strategies were applied to try to mitigate the difficulties caused by the large difference in the number of images in each class, in this case, lesions that are melanoma and lesions that are not melanoma: reducing the number of samples in the dataset to balance it, applying data augmentation and applying class weights. In addition, methods for optimizing the training process of a Convolutional Neural Network are successfully applied to automate the hyperparameters selection process and the training time of models that use large neural networks as feature extractors is reduced because of it. The data augmentation and class weights adopted in this work helped the training procedure but were not enough to produce a large improvement in performance, but the latter method was applied in the best result obtained.