Abstract
Background: Among skin cancers, melanoma is the most dangerous and aggressive form, exhibiting a high mortality rate worldwide. Biopsy and histopatholog-ical analysis are common procedures for skin cancer detection and prevention in clinical settings. A significant step involved in the diagnosis process is the deep understanding of patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual seg-mentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time which makes its prediction challenging. Moreover, at the initial stage, it is difficult to predict melanoma as it closely resembles other skin cancer types that are not malignant as melanoma, thus automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. Methods: As deep learning approaches have gained high attention in recent years due to their remarkable performance, therefore, in this work, we proposed a novel, end-to-end atrous spatial pyramid pooling based convolutional neural network (CNN) framework for automatic lesion segmentation. This architecture is built based on the concept of atrous dilated convolutions which are effective for semantic segmentation. A dense deep neural network is designed using several building blocks consisting of convolutional, batch normalization, leaky ReLU layer with fine-tuning of hyperparameters contributing towards higher performance. Conclusion: The network was tested using three benchmark datasets by International Skin Imaging Collaboration, i.e. ISIC 2016, ISIC 2017, and ISIC 2018. The experimental results showed that the proposed network achieved an average Jac-card index of 86.5% on ISIC 2016, 81.2% on ISIC 2017, and 81.2% on ISIC 2018 datasets, respectively which is recorded as higher than the top three winners of the ISIC challenge. Also, the model successfully extracts lesions from the whole image in one pass, requiring no pre-processing process. The conclusions yielded that network is accurate in performing lesion segmentation on skin cancer images.
Automatic Lesion Segmentation Using Atrous Convolutional Deep Neural Networks in Dermoscopic Skin Cancer Images
