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Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.

S Gayathri¹, Varun P Gopi², P Palanisamy¹

¹National Institute of Technology, Tiruchirappalli, Tamil Nadu, India.

Physical and Engineering Sciences in Medicine

|May 25, 2021

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An automated method using Multipath Convolutional Neural Network (M-CNN) and J48 classifier achieves 99.62% accuracy for Diabetic Retinopathy (DR) grading. This approach enables early disease detection and accurate severity categorization from fundus images.

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic Retinopathy (DR) diagnosis relies on manual fundoscopy by eye care professionals.
Early detection and precise grading of DR are crucial for effective patient management.
Current methods may lack the speed and accuracy needed for widespread screening.

Purpose of the Study:

To develop an automated system for Diabetic Retinopathy grading using deep learning and machine learning.
To extract and categorize features from fundus images based on DR severity.
To enhance early DR detection and improve patient care pathways.

Main Methods:

Utilized a Multipath Convolutional Neural Network (M-CNN) for extracting global and local image features.
Employed machine learning classifiers, including Support Vector Machine (SVM), Random Forest, and J48, for DR severity categorization.

Keywords:

DR grading Machine Learning classifiers Multipath CNN (MCNN)Retinal fundus images Retinal lesions

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Evaluated the model on diverse public datasets: IDRiD, Kaggle (DR detection), and MESSIDOR.

Main Results:

The M-CNN combined with the J48 classifier demonstrated superior performance.
Achieved an average accuracy of 99.62% for Diabetic Retinopathy grading.
The system showed high effectiveness in both DR grading and early disease detection.

Conclusions:

The proposed automated method offers a highly accurate and efficient solution for Diabetic Retinopathy grading.
This AI-driven approach has the potential to significantly aid in early DR detection and management.
The M-CNN and J48 classifier combination provides a robust tool for clinical application in ophthalmology.