Original scientific paper
https://doi.org/10.1080/00051144.2024.2363692
Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping
Abderaouf M. Moustari
; LASS Laboratory, Faculty of Technology, University Mohamed Boudiaf of M’sila, M’sila, Algeria
Youcef Brik
; LASS Laboratory, Faculty of Technology, University Mohamed Boudiaf of M’sila, M’sila, Algeria
*
Bilal Moustari
; LASS Laboratory, Faculty of Technology, University Mohamed Boudiaf of M’sila, M’sila, Algeria
Rafik Bouaouina
; PIMIS Laboratory, Electronics and Telecommunications Department, University 08 Mai 1954 of Guelma, Guelma, Algeria
* Corresponding author.
Abstract
The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions
scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually localized. Training Convolutional
Neural Networks (CNNs) on global images can be challenging due to excessive noise. Therefore, it’s crucial to enhance the visibility of important regions and focus the recognition system
on them to improve accuracy. This study investigates the task of classifying the severity of diabetic retinopathy in eye fundus images by employing appropriate preprocessing techniques to
enhance image quality. We propose a novel two-branch attention-guided convolutional neural
network (AG-CNN) with initial image preprocessing to address these issues. The AG-CNN initially
establishes overall attention to the entire image with the global branch and then incorporates a
local branch to compensate for any lost discriminative cues. We conduct extensive experiments
using the APTOS 2019 DR dataset. Our baseline model, DenseNet-121, achieves average accuracy/AUC values of 0.9746/0.995, respectively. Upon integrating the local branch, the AG-CNN
improves the average accuracy/AUC to 0.9848/0.998, representing a significant advancement in
state-of-the-art performance within the field.
Keywords
Gradient weighted class activation mapping; deep learning; diabetic retinopathy classification; two-stage system; image preprocessing; region of interest extration
Hrčak ID:
326280
URI
Publication date:
27.6.2024.
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