ADVANCING CORNEAL ULCER DIAGNOSIS: LEVERAGING PREDICTIVE ANALYSIS THROUGH DEEP CONVOLUTIONAL GAN IMAGE CLASSIFICATION

Abstract

1.      Corneal ulcers present a formidable challenge to ocular health, necessitating swift and precise diagnosis for effective therapeutic intervention. This study pioneers an innovative approach to corneal ulcer diagnosis, harnessing the cutting-edge capabilities of deep learning. Through the integration of predictive analysis and Deep Convolutional Generative Adversarial Network (DCGAN) image classification, a novel diagnostic framework emerges and it is implemented in Python software. The methodology commences with the meticulous acquisition of a comprehensive dataset encompassing corneal ulcer images sourced from medical databases. Guided by this rich dataset, a meticulously tailored DCGAN architecture is meticulously crafted and trained on the designated dataset. This process yields synthetic corneal ulcer images, thereby amplifying dataset diversity and facilitating enhanced model training. Following this, a sophisticated deep Convolutional Neural Network (CNN) is engaged for image classification, strategically leveraging both authentic and synthetic images to optimize model performance. Evaluation of this pioneering methodology unveils compelling results in corneal ulcer diagnosis, characterized by exemplary accuracy of 99.12% which is 13.57% higher than AlexNet, VGG16 and m-VGG. The interpretations of the model's predictions, meticulously validated in collaboration with esteemed medical professionals, underscore its pivotal role as a potent diagnostic instrument. In essence, this research epitomizes a monumental leap forward in corneal ulcer diagnosis, epitomizing the synergy between predictive analysis and deep learning methodologies. By seamlessly integrating advanced computational techniques with clinical expertise, this study not only expands the horizons of diagnostic precision but also underscores the transformative potential of interdisciplinary collaboration in healthcare innovation