Intelligent Application of Laser for Medical Prognosis: An Instance for Laser Mark Diabetic Retinopathy

Sumit Das^1*, Dipansu Mondal² and Diprajyoti Majumdar¹

¹JIS College of Engineering, Department of Information Technology, Kalyani, WB, India

²University of Kalyani, CIRM, WB, India

Corresponding Author E-mail:sumit.das@jiscollege.ac.in

DOI : http://dx.doi.org/10.13005/bbra/3109

ABSTRACT: Refractive laser surgery is all about the accuracy, whether screening or surgery, given the age and profile of the patient enduring these trials, there is no margin for error. Most of them are for aesthetic reasons, contact lens intolerance, or professional reasons, including athletes. In this article, the role of artificial intelligence and deep learning in laser eye surgeries has been introduced. The presence of lingering laser spots on the retina after refractive laser surgery in diabetic retinopathy poses a potential risk to visual integrity and ocular well-being. The hypothesis for the research paper is that the hybridized convolutional neural network models, including LeNet-1, AlexNet, VGG16, PolyNet, Inception V2, and Inception-ResNetV2, will yield varying levels of performance in classifying and segmenting laser spots in the retina after diabetic retinopathy surgery. The hypothesis predicts that Inception-ResNetV2 will demonstrate superior results compared to the other CNN versions. The research aims to provide a novel approach for laser therapies and treatments, facilitating the rapid classification, highlighting, and segmentation of laser marks on the retina for prompt medical precautions.  The comparative analysis revealed that Inception-ResNetV2 exhibited exceptional performance in both training and validation, achieving the highest accuracy (96.54%) for classifying diabetic retinopathy images. Notably, VGG16 also demonstrated strong performance with a validation accuracy of 94%. Conversely, LeNet-1, AlexNet, PolyNet, and Inception V2 displayed comparatively lower accuracy rates, suggesting their architectures may be less optimized for this particular image classification task. This achievement holds immense promise for timely detection, precise localization, and optimal management of laser spots, fostering enhanced visual outcomes and elevating the standards of patient care in this context.

KEYWORDS: Biomedical image processing; Convolutional Neural Networks(CNN); Classification algorithms; Diabetic Retinopathy; Feature extraction; Laser Mark

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