Advances in Deep Learning for Skin Cancer Diagnosis

Abstract

The most prevalent type of cancer worldwide is known as skin cancer. Early detection is critical because if left undiagnosed in the primary stage, it might be fatal. Although there are differences within the class and high inter-class similarities, it is too difficult to distinguish with the naked eye. Owing to the disease's global prevalence, a number of deep learning based automated systems were created thus far to help doctors identify skin lesions early on. Using pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks (CNNs), we trained VGG19 on the HAM10000 dataset. The optimal performance was observed with FT. The model that was created, which yielded an accuracy that was greater overall than the one used in transfer learning, was 82.4±1.9 %. By offering a second opinion and supporting the clinician's diagnosis, this performance could lower morbidity and treatment costs.