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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Sundararajan, Sridhevi
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Computer Science & IT Engineering

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Artificial intelligence-enabled profiling of overlapping retinal disease distribution for ocular diagnosis Sundararajan, Sridhevi; Ramachandran, Harikrishnan; Gupta, Harshitha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2713-2724

Abstract

Eyesight, an invaluable gift profoundly impacts our daily lives. In a rapidly evolving healthcare landscape, the preservation and enhancement of ocular health stand as critical objectives. This research endeavors to analyze the two retinal fundus multi-disease image datasets (RFMiD) one containing 3200 images and the other containing 860 fundus images. The primary objective of this study is to scrutinize these datasets, discern variations in the frequency of labeled diseases within and across them, and explore common combinations of labels. These findings hold important implications for the field of retinal image analysis, as they provide valuable insights into the distribution and co-occurrence of defects.
Predicting enhanced diagnostic models: deep learning for multi-label retinal disease classification Sundararajan, Sridhevi; Ramachandran, Harikrishnan; Gupta, Harshita; Patil, Yashraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp54-61

Abstract

In this study, we assess three convolutional neural network (CNN) architectures—VGG16, ResNet50, and InceptionV3 for multi classification of fundus images in the retinal fundus multi-disease image dataset (RFMID2), comprising of 860 images. Focusing on diabetic retinopathy, exudation, and hemorrhagic retinopathy, we preprocessed the dataset for uniformity and balance. Using transfer learning, the models were adapted for feature extraction and fine-tuned to our multi-label classification task. Their performance was measured by subset accuracy, precision, recall, F1-score, hamming loss, and Jaccard score. VGG16 emerged as the top performer, with the highest subset accuracy (84.81%) and macro precision (95.83%), indicating its superior class distinction capabilities. ResNet50 showed commendable accuracy (79.75%) and precision (86.70%), whereas InceptionV3 lagged with lower accuracy (66.67%) and precision (81.21%). These findings suggest VGG16’s depth offers advantages in multi-label classification, highlighting InceptionV3’s limitations in complex scenarios. This analysis helps optimize CNN architecture selection for specific tasks, suggesting future exploration of dataset variability, ensemble methods, and hybrid models for improved performance.
Co-Authors Gupta, Harshita Gupta, Harshitha Patil, Yashraj Ramachandran, Harikrishnan