Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma
Adrian D. Bandy, Yannis Spyridis, Barbara Villarini, Vasileios Argyriou
Abstract
This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%.
Topics & Concepts
Convolutional neural networkCluster analysisArtificial intelligenceComputer scienceMelanomaPattern recognition (psychology)Artificial neural networkFocus (optics)Receiver operating characteristicMachine learningMedicineCancer researchOpticsPhysicsCutaneous Melanoma Detection and ManagementAI in cancer detectionCell Image Analysis Techniques