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

Amal Alshardan, Saad Alahmari, Mohammed Alghamdi, Mutasim Al Sadig, Abdullah Mohamed and Gouse Pasha Mohammed
Additional contact information
Amal Alshardan: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia
Saad Alahmari: ��Department of Computer Science, Applied College, Northern Border University, Arar, Saudi Arabia
Mohammed Alghamdi: ��Department of Information Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia
Mutasim Al Sadig: �Department of Computer Science, College of Science, Majmaah University, Al Majmaah 11952, Saudi Arabia
Abdullah Mohamed: �Research Centre, Future University in Egypt, New Cairo 11845, Egypt
Gouse Pasha Mohammed: ��Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-14

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.

Keywords: Generative Adversarial Networks; Dermoscopic Image Analysis; Social Network Search; Feature Fusion; Deep Learning; Complex Systems (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25400390

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