Forward selection-based ensemble of deep neural networks for melanoma classification in dermoscopy images

Melanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection-based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size-related various properties of dermoscopy images. To this end, EfficientNet models B3-B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross-validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.