GAN-BASED SYNTHETIC MEDICAL IMAGE AUGMENTATION FOR CLASS IMBALANCED DERMOSCOPIC IMAGE ANALYSIS

Abstract

AI-generated content (AIGC) in the context of dermoscopic image analysis describes the application of artificial intelligence (AI) approaches to produce synthetic images for training and enriching machine learning (ML) methods. In dermatology, where dermoscopic images are essential in skin lesion detection, AIGC helps overcome the challenges of inadequate datasets and class imbalances. By employing advanced generative complex systems like Generative Adversarial Networks (GANs), AIGC will simulate different dermoscopic conditions by generating realistic-view synthetic images. These AI-generated images help to enhance the training dataset, contributing to the ML models with many widespread and varied sets of instances. This aids in increasing the ability of the model to precisely predict and generalize while handling new, unnoticed dermoscopic images. Deep learning (DL) methods, specifically convolutional neural networks (CNNs), can provide a massive breakthrough in an extensive range of computer vision (CV) tasks of complex systems, predominantly by exploiting large-scale annotated datasets. This paper develops a GAN-based Synthetic Medical Image Augmentation for the imbalanced Dermoscopic Image Analysis (GANSMIA-CDIA) method. The GANSMIA-CDIA technique’s primary target is exploiting the GAN model for synthetic image generation to handle class imbalance data problems. In addition, the GANSMIA-CDIA technique diagnoses the melanoma using the optimal DL model. The GANSMIA-CDIA technique applies a contrast enhancement process for the noise eradication process. The GANSMIA-CDIA technique follows a feature fusion method comprising different DL methods such as MobileNetV2, AlexNet, ResNet50, and Inceptionv3 to learn feature patterns in the preprocessed images. Meanwhile, the Social Network Search (SNS) technique is utilized for the hyperparameter tuning process. Also, the bidirectional long short-term memory (BiLSTM) technique is implemented to detect and classify melanoma. A series of simulations were performed on the standard dermoscopic image dataset to evaluate the performance of the GANSMIA-CDIA technique. The experimental values indicate the excellence of the GANSMIA-CDIA technique over existing techniques.