Enhancing Dermatological Diagnostics: An Enhanced Approach for Skin Cancer Classification Using pix2pix GAN

Skin cancer is among the predominant forms of the disease that includes malignant squamous cell carcinoma, basal cell carcinoma, and melanoma that is characterized by aberrant melanocyte cell development. Frequent screenings and examinations enhance the prognosis for people with skin cancer. Sadly, a lot of patients with skin cancer are not diagnosed until the condition has progressed past the point at which treatment is effective. Deep learning techniques in computer vision have made impressive strides, but issues like class imbalance and a lack of data still hinder the autonomous identification of skin conditions. A solution to address these problems is the implementation of GAN, which is capable of synthesizing realistic data. In this paper, a deep learning GAN model for image synthesis utilizing the pix2pixHD integrated with Convolutional Neural Network (CNN) classifier approach is used to perform skin cancer classification. To categorize three forms of skin cancer benign or malignant. The proposed pix2pixHD GAN is a novel method for utilizing pertinent skin lesion information for generation of high-quality synthesized dermoscopic image and conduct skin lesion classification performance with improved accuracy. Realistic images were created using a U-Net-based generator and PatchGAN discriminator with custom CNN architecture to classify three forms of cancer. With remarkable accuracy of 87.65% (MEL), 91% (BCC), and 89.85% (SCC) and other performance parameters indicate that GAN pix2pixHD Classifier model has promising results in classification. These findings demonstrate the Classifier's ability to produce and correctly identify high-quality skin lesion images, indicating its potential as a deep learning-based medical image analysis tool.