Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

Abstract Melanoma is the most fatal type of skin cancer which can cause the death of victims at the advanced stage. Extensive work has been presented by the researcher on computer vision for skin lesion localization. However, correct and effective melanoma segmentation is still a tough job because of the extensive variations found in the shape, color, and sizes of skin moles. Moreover, the presence of light and brightness variations further complicates the segmentation task. We have presented improved deep learning (DL)‐based approach, namely, the DenseNet77‐based UNET model. More clearly, we have introduced the DenseNet77 network at the encoder unit of the UNET approach to computing the more representative set of image features. The calculated keypoints are later segmented by the decoder of the UNET model. We have used two standard datasets, namely, the ISIC‐2017 and ISIC‐2018 to evaluate the performance of the proposed approach and acquired the segmentation accuracies of 99.21% and 99.51% for the ISIC‐2017 and ISIC‐2018 datasets, respectively. We have confirmed through both the quantitative and qualitative results that the proposed improved UNET approach is robust to skin lesions segmentation and can accurately recognize the moles of varying colors and sizes.