Disease Prediction based on Retinal Images

Shivappriya, S. N.; Rajaguru, Harikumar; Ramya, M.; Asiyabegum, U.; Prasanth, D.

doi:10.1109/stcr51658.2021.9588829

Cited by 3 publications

(1 citation statement)

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“…The model is then compiled with suitable parameters, such as the Adam optimizer and sparse categorical cross entropy loss, optimizing it for accurate prediction. By employing the VGG model in eye disease classification and prediction, researchers and practitioners benefit from a robust framework that combines the model's innate ability to understand complex visual patterns with task specific adaptations, ultimately contributing to advancements in ophthalmic diagnostics and patient care [8].…”

Section: B Feature Extractionmentioning

confidence: 99%

Comprehensive Survey on Disease Detection in Retinal Imaging through Deep Learning Techniques

Deore,

Inamdar,

Nimkar

et al. 2024

IJRASET

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Millions of people around the world suffer from visual impairment and blindness that could have been prevented or diagnosed earlier. To tackle this issue, we’ve introduced a groundbreaking approach to detect multiple eye diseases using retinal imaging. Our study involves a unique combination of various advanced deep learning models through ensemble learning.We present a detailed examination of deep learning models in the context of multi-disease classification using the Retinal Fundus MultiDisease Image Dataset (RFMiD). The objective is to assess the performance of these models and identify the most effective one for accurate and reliable disease diagnosis.We begin by preprocessing the RFMiD dataset to enhance image quality and extract relevant features, ensuring a robust input for our models. Subsequently, each model undergoes a comprehensive training and finetuning process to optimize its parameters for disease classification. The evaluation metrics include accuracy, precision, recall, and F1 score, providing a comprehensive understanding of model performance.We discuss the strengths and weaknesses of each model, shedding light on factors influencing their performance. The insights gained from this comparative analysis can guide researchers and practitioners in selecting an appropriate model for retinal disease diagnosis based on specific requirements and constraints.In addition, we explore potential avenues for future research, including ensemble methods and hybrid architectures, to further improve classification accuracy. The paper includes discussion on the practical implications of our findings and their significance in advancing the field of medical image analysis, particularly in the context of retinal disease diagnosis.

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Section: B Feature Extractionmentioning

confidence: 99%