Unveiling the Depths of Stromal Layer Analysis with Deep Learning: Harnessing Mobilenet V2 Algorithm for Comprehensive Insights

Stromal layer in the cornea is important to flow aqueous liquid in eye to flow in and out by leveraging deep learning model mobile net V2 the DL model identifies the various statistical parameters such as cell detection, density measurement, and abnormality identification within corneal image, Mobile net V2 is well known for accuracy and S-PSO model integrated with mobile net V2, which undergoes rigorous training for both normal and disease corneal image identification. Leveraging the strengths of MobileNet V2, our system demonstrates exceptional efficiency in handling complex corneal image data, enabling precise and rapid analysis. The S-PSO model, empowered by MobileNet V2, undergoes rigorous evaluation through the analysis of both normal and disease-associated corneal images. The results obtained are not only promising but also reveal significant advantages over conventional methods. Specifically, when compared to manual assessments and two morphological-based approaches, the S-PSO model showcases average differences of 5%, 7%, and 13% in cell density calculations. This highlights the superior accuracy and efficiency of the MobileNet V2-infused S-PSO model in extracting meaningful insights from corneal images. Positioned as a transformative clinical tool, our developed system goes beyond merely incorporating cutting-edge technology; it significantly enhances ophthalmologists' capabilities in interpreting confocal corneal images. The symbiotic integration of MobileNet V2 with the S-PSO approach not only addresses the challenges of corneal health assessment but also lays a robust foundation for future advancements at the intersection of deep learning and ophthalmology