An investigation of retinal descriptors on Indian database for automatic pathological diagnosis and classification of retinopathy of prematurity
by Sushma S. Kadge; Sanjay L. Nalbalwar; Anil B. Nandgaonkar; Parag K. Shah; V. Narendran
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 42, No. 4, 2023

Abstract: Early diagnosis is crucial to prevent blindness in preterm neonates. Scarcity of specialists indicates an urgent need for automated identification, classification and diagnosis of retinopathy of prematurity (ROP). Previous automation works did not consider the classification of ROP stages which is an essential decision maker in treatment. We developed an automatic assessment system for ROP classification (AASRC) on Indian databases. We studied stochastic gradient descent (SGD) along with five other classifiers. 64 experiments were conducted to explore the intra database based on grey-level co-occurrence matrix (GLCM) descriptors using various frequency-based parameters for classification of ROP. Information gain (IG) scoring function is used to identify best descriptor while student's t-tests is used for validation. The classification accuracy rate of ROP disorder achieved are 99.03%, 93.87%, 94.55%, 92.51% and 97.95% respectively for normal/abnormal, stages 1, 2, 3, and 4. The experimental findings demonstrate the proposed feature descriptors and classifier outperforms state-of-the-art models.

Online publication date: Mon, 14-Aug-2023

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