Proposing an Efficient Model to Detect Melanoma Based on Dense-CNN
Abstract - Deep learning algorithms provide the best judgment aid for medical experts, potentially improving the classification of skin lesions and early detection of skin cancer. In manycases, deep learning techniques such as neural networks have bypassed feature extraction tools. The aim of this study is to design a neural network that has the ability to accurately and efficiently classify images of skin lesions. In this paper, a dense convolutional neural network (Dense-CNN) model is designed for melanoma diagnosis. The proposed model was applied to two datasets: the HAM10000 dataset and the DermQuest-DermIS dataset. These datasets are for images of skin lesions obtained through dermatoscopy. Our results were compared with those of other studies. The proposed model achieved high diagnostic rates in terms of accuracy, sensitivity, specificity, and precision when applied to the HAM10000 dataset, reaching 98.03%, 98.05%, 99.67%, and 98.11%, respectively, while the results when applied to the DermQuest-DermIS dataset reached 97.85%, 97.87%,
Keywords - Melanoma, Dermoscopy, Convolutional Neural Networks, HAM10000, Derm Quest-DermIS.