An Explainable Deep Learning Framework for Skin Cancer Detection Using Dermoscopic Images

Abstract

Skin cancer is a significant global health concern, where early and accurate detection is important for supporting effective treatment and improving patient outcomes. To perform manual examination of the skin lesions is time consuming and could rely significantly on clinical expertise, which may lead to the need of computer aided approaches helping to classify skin lesions. In this study, an automated, DL approach to classifying dermoscopic skin images as benign or malignant is proposed. In this research, a publicly available Kaggle skin cancer dataset was used, containing 3600 images with labels, half of which were malignant and the other half were benign. The data set was split into 70% training, 15% validation and 15% testing sets. The input images were preprocessed and enhanced using image processing and augmentation techniques to standardize images, enhance the diversity of training samples, and enhance model generalization. Transfer learning was explored in four different pretrained convolutional neural network (CNN) architectures. The models were trained under similar experimental conditions analysis were used for the evaluation. Of the evaluated architectures, MobileNetV2 had the best overall classification performance with an accuracy of 91.11%. The accuracy of DenseNet201 was 88.06%, higher than that of ResNet50V2 (86.81%) and Xception (85.83%). To explore the learning behavior of the models, training and validation accuracy and loss curves were also analyzed, in addition to the quantitative evaluation. Furthermore, an explainable artificial intelligence method named Grad-CAM was added to visualize the image regions that the model relied on to make its prediction, thus gaining another insight into the model's decision-making behavior of the CNN architectures. The results show that both the CNN architectures used for the experiment and the automated classification of skin lesions using them are effective, and the MobileNetV2 performs the best in the experimental framework of this study. The proposed framework can serve as a foundation for the development of efficient and interpretable computer-aided skin lesion classification systems.