Generative Adversarial Networks for Endoscopic Image Classification

Generative Adversarial Networks (GANs) have shown tremendous potential in medical image processing, particularly for the classification of endoscopic images. Endoscopic Image Classification using Machine Learning Algorithms (EIC-MLA) leverages GANs to enhance image generation and classification accuracy, enabling better diagnosis in gastrointestinal examinations. Existing methods for endoscopic image classification often suffer from low accuracy due to challenges such as poor image quality, limited datasets, and difficulty in differentiating between healthy and abnormal tissues. These issues hinder the reliability and precision of automated diagnostic systems. The proposed EIC-MLA framework addresses these limitations by utilizing a Generative Adversarial Network (GAN) model to improve data augmentation and enhance image quality, which is critical for accurate classification. The GAN-based framework generates high-quality synthetic images that closely mimic real endoscopic images, thereby expanding the training dataset and improving the performance of the classification algorithm. This method applies GAN-generated images in conjunction with a machine learning classifier to improve the differentiation between normal and abnormal tissue, enhancing diagnostic reliability. The integration of deep learning with GANs allows for a more robust classification model that can handle variations in image quality and appearance. The outcome of the proposed method demonstrates improved accuracy in endoscopic image classification, offering more precise and reliable diagnostic support. The enhanced performance of the EIC-MLA framework suggests that it could significantly contribute to the early detection of gastrointestinal diseases, improving patient outcomes. The EIC-MLA framework demonstrates exceptional performance, achieving 97.14% accuracy, 93.17% early detection, 98.29% improved patient outcomes, 95.11% image quality enhancement, and 98.31 % diagnostic reliability.