Research Article
Deep Learning Model Evaluation and Insights in Inherited Retinal Disease Detection
@INPROCEEDINGS{10.1007/978-3-031-60665-6_22, author={H\^{e}lder Ferreira and Ana Marta and In\"{e}s Couto and Jos\^{e} C\~{a}mara and Jo\"{a}o Melo Beir\"{a}o and Ant\^{o}nio Cunha}, title={Deep Learning Model Evaluation and Insights in Inherited Retinal Disease Detection}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={}, doi={10.1007/978-3-031-60665-6_22} }- Hélder Ferreira
Ana Marta
Inês Couto
José Câmara
João Melo Beirão
António Cunha
Year: 2024
Deep Learning Model Evaluation and Insights in Inherited Retinal Disease Detection
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_22
Abstract
Inherited retinal diseases such as Retinitis Pigmentosa and Stargardt’s disease are genetic conditions that cause the photoreceptors in the retina to deteriorate over time. This can lead to vision symptoms such as tubular vision, loss of central vision, and nyctalopia (difficulty seeing in low light) or photophobia (high light). Timely healthcare intervention is critical, as most forms of these conditions are currently untreatable and usually focused on minimizing further vision loss.
Machine learning (ML) algorithms can play a crucial role in the detection of retinal diseases, especially considering the recent advancements in retinal imaging devices and the limited availability of public datasets on these diseases. These algorithms have the potential to help researchers gain new insights into disease progression from previous classified eye scans and genetic profiles of patients.
In this work, multi-class identification between the retinal diseases Retinitis Pigmentosa, Stargardt Disease, and Cone-Rod Dystrophy was performed using three pretrained models, ResNet101, ResNet50, and VGG19 as baseline models, after shown to be effective in our computer vision task. These models were trained and validated on two datasets of autofluorescent retinal images, the first containing raw data, and the second dataset was improved with cropping to obtain better results. The best results were achieved using the ResNet101 model on the improved dataset with an Accuracy (Acc) of 0.903, an Area under the ROC Curve (AUC) of 0.976, an F1-Score of 0.897, a Recall (REC) of 0.903, and a Precision (PRE) of 0.910.
To further assess the reliability of these models for future data, an Explainable AI (XAI) analysis was conducted, employing Grad-Cam. Overall, the study showed promising capabilities of Deep Learning for the diagnosis of retinal diseases using medical imaging.
