Enhanced skin cancer classification via Xception model

Abstract

Skin cancer is a prevalent and deadly cancer, and early detection is crucial for improving treatment success. Intelligent technologies are currently being used to classify skin lesions. The fundamental goal of this experimental research is to investigate biomedical skin cancer datasets to develop an effective approach for determining whether a cancer is malignant or benign. Well-known deep learning classification models (convolutional neural network (CNN) (sequential), ResNet50, InceptionV3, and Xception) are employed to train and categorize the dataset images. Two large and balanced datasets are collected and employed in this research. One is used to compare the performance of the employed model algorithms. Next, the selected model(s) are again trained on the second dataset for validation and generalization purposes. It turns out that the performance of the Xception model is superior and can be generalized. The performance results obtained from various simulations are tabulated and graphed. Comparative results are also presented.