Transfer-learning based skin cancer diagnosis using fine-tuned AlexNet by Marine Predators Algorithm - Ibrahim Khaleel, Maha; Lakizadeh, Amir (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Melanoma represents one of the most dangerous manifestations of skin cancer. According to statistics, 55% of patients with skin cancer have lost their lives as a result of this disease. Early diagnosis of this condition will significantly reduce mortality rates and save lives. In recent years, deep learning methods have shown promising results and captured the attention of researchers in this field. One common approach is the use of pre-trained deep neural networks. In this work, a pre-trained AlexNet networks, which are networks with specified architecture and weights is used to automatic skin melanoma diagnosis.  In the transfer learning phase, by reducing the learning rate, the pre-trained network is trained to recognize Skin cancer, which is called fine-tuning. In addition, Hyperparameters of the AlexNet network have been optimized by the Marine Predators Algorithm (MPA) to enhance the network performance. Experimental findings show the satisfactory efficiency of the presented approach, with an accuracy rate of 98.47%. The outcomes demonstrate the effectiveness of the suggested approach in contrast to alternative existing methods.