GAN based augmentation using a hybrid loss function for dermoscopy images

Dermatology is the most appropriate field to utilize pattern recognition-based automated techniques for objective, accurate, and rapid diagnosis because diagnosis mainly relies on visual examinations of skin lesions. Recent approaches utilizing deep learning techniques have shown remarkable results in this field. However, they necessitate a substantial quantity of images and the availability of dermoscopy images is often limited. Also, even if enough images are available, their labeling requires expert knowledge and is time-consuming. To overcome these issues, an efficient augmentation approach is needed to expand training datasets from input images. Therefore, in this work, a generative adversarial network has been developed using a new hybrid loss function constructed with traditional loss functions to enhance the generation power of the architecture. Also, the effect of the proposed approach and different generative network-based augmentations, which have been used with dermoscopy images in the literature, on the classification of skin lesions has been investigated. Therefore, the main contributions of this work are: (i) introducing a new generative model for the augmentation of dermoscopy images; (ii) presenting the effect of the proposed model on the classification of the images; (iii) comparative evaluations of the effectiveness of different generative network-based augmentations in the classification of seven forms of skin lesions. The classification accuracy when the proposed augmentation is used is 93.12%, which is higher than its counterparts. Experimental results indicate the significance of augmentation techniques in the classification of skin lesions and the efficiency of the proposed structure in improving the classification accuracy.