Efficient Automated Diagnosis of Retinopathy of Prematurity by Customize CNN Models

TL;DR

This study develops and evaluates customized CNN models for accurate, efficient, and computationally feasible automated diagnosis of Retinopathy of Prematurity, demonstrating improved performance over pre-trained models and practical deployment potential.

Contribution

The paper introduces tailored CNN architectures specifically optimized for ROP diagnosis, outperforming pre-trained models in accuracy and efficiency, and explores deployment in clinical settings.

Findings

Customized CNNs achieve higher accuracy and F1-scores.

Voting system enhances diagnostic performance.

Models are feasible for deployment in clinical environments.

Abstract

This paper encompasses an in-depth examination of Retinopathy of Prematurity (ROP) diagnosis, employing advanced deep learning methodologies. Our focus centers on refining and evaluating CNN-based approaches for precise and efficient ROP detection. We navigate the complexities of dataset curation, preprocessing strategies, and model architecture, aligning with research objectives encompassing model effectiveness, computational cost analysis, and time complexity assessment. Results underscore the supremacy of tailored CNN models over pre-trained counterparts, evident in heightened accuracy and F1-scores. Implementation of a voting system further enhances performance. Additionally, our study reveals the potential of the proposed customized CNN model to alleviate computational burdens associated with deep neural networks. Furthermore, we showcase the feasibility of deploying these models within dedicated software and hardware configurations, highlighting their utility as valuable diagnostic aids in clinical settings. In summary, our discourse significantly contributes to ROP diagnosis, unveiling the efficacy of deep learning models in enhancing diagnostic precision and efficiency.