HEXA-GAN: Skin lesion image inpainting via hexagonal sampling based generative adversarial network

Skin cancer is the uncontrollable proliferation of abnormal cells in the epidermis. In manual examination, the dermoscopic images are used by clinicians to detect skin cancer. However, digital hair removal (DHR) is more challenging task due to the presence of the hair occlusion that disturbs the intrinsic intensities and geometric features of the lesion regions that negatively affect diagnosis. Some of the dermis hairs in dermoscopic images are thicker, thinner, overlaps, fades, and obscures or covers textural changes. To overcome these challenges, a novel deep learning based Hexa-GAN model is proposed for hair segmentation and hair gap inpainting. Initially, the images from ISIC dataset are given to Attention U-net for segmenting the hairs in pixel units. The masked hair gap images from Attention U-net are given to the proposed Hexa-GAN with ReLU activation function. The first GAN (G1) performs coarse inpainting and reconstructs images using hexagonal sampling. Hexagonal divisions are merged into coarse inpainted images. The output of G1 is fed into the second GAN (G2) for fine reconstruction with contextual attention layer to generate the missing patches of the image. Finally, the output from G2 is merged into the original inpainted images without hair regions. From the experimental analysis, the Attention-Unet achieves better performance based on Jaccard index of 94.64% and Dice Index of 82.61% respectively. The proposed Hexa-GAN model increases the average PSNR range by 9.89%, 3.83%, 2.75%, 5.68%, 4.87% and 6.11% compared to CHC-Otsu model, Encoder-Decoder CNN, SN-PatchGAN, Variational Autoen-coder, AlexNet architecture and FCN8-ResNetC approach respectively.