The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach

Abstract

Skin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present significant challenges. To address this challenge, our study employs variants of Convolutional Neural Networks (CNNs) to effectively diagnose and classify various skin lesion types using the latest benchmark datasets ISIC 2019 and 2020. The dataset underwent rigorous preprocessing, which involves employing advanced Generative Artificial Intelligence (AI) techniques i.e., Generative Adversarial Networks (GANs) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), for augmentation. These generative techniques are carefully evaluated and compared for their effectiveness. Our CNN-based approach involves aggregating results from multiple transfer learning models, including VGG16, VGG19, SVM along with a hybrid model in combination of VGG19 and SVM. On ISIC 2019, we have achieved promising accuracies of 92% for VGG16 and 93% for VGG19. Notably, the hybrid VGG19+SVM model exhibits the highest accuracy of 96%. On ISIC 2020, VGG16, VGG19, and SVM achieves accuracies of 90%, 92%, and 92%, respectively. Our findings underscore the potential of generative AI for augmentation, and the efficacy of CNN-based transfer learning models in improving skin cancer classification accuracy.

Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda

“…-Basic augmentation (involving geometric transformations, cropping, occlusion, intensity operations, noise injection, filtering, and combinations) Studies (Perez et al, 2018;Abayomi-Alli et al, 2021;Rezk et al, 2022;Saeed et al, 2023) highlight the positive impact of using data augmentation techniques to expand training sets on skin conditions and classification models, including increasing the number of images for POC, which is already very sparse. Although augmentation enhances data diversity, it introduces the risk of generating synthetic patterns that may not accurately represent real data, potentially affecting the model's performance.…”