Optimizing Pix2Pix GAN with Attention Mechanisms for AI-Driven Polyp Segmentation in IoMT-Enabled Smart Healthcare.

Abstract

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing prevalent challenges in conventional segmentation methods, such as variable polyp appearances, inconsistent image quality, and limited training data, our model significantly augments the precision and reliability of polyp segmentation. The integration of an attention mechanism enables our model to meticulously focus on the intricate features of polyps, improving segmentation accuracy. A unique training strategy, employing both real and synthetic data, is adopted to ensure the model's robust performance under a variety of conditions. The results, validated through rigorous tests on multiple public colonoscopy datasets, indicate a notable improvement in segmentation performance over existing state-of-the-art methods. Our model's enhanced ability to detect critical details early plays a pivotal role in proactive colorectal cancer detection, a key aspect of smart healthcare systems. This work represents an effective amalgamation of advanced AI techniques and the Internet of Medical Things (IoMT), signifying a noteworthy contribution to the evolution of smart healthcare systems. In conclusion, our attention-enhanced Pix2Pix GAN not only offers efficient and reliable polyp segmentation, but also showcases considerable potential for seamless integration into remote health monitoring systems, underlining the increasing relevance and efficacy of AI in advancing IoMT-enabled healthcare.