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An automated diabetic retinopathy of severity grade classification using transfer learning and fine-tuning for fundus images

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Abstract

Diabetes mellitus Retinopathy (DR) has recently become a major health problem, and its complications are also increasing worldwide. Early diagnosis of DR is essential to determine the significance of several features from fundus images for detection and classification in many Computer-Aided Diagnosis (CAD) applications. However, existing methods suffer from high dimensional features, small training datasets, misclassification, and high training loss, which leads to a complex grading system. Aiming at these concerns, this paper presents a Frame-wise Severity Scale Classification Model (FSSCM) using Transfer Learning enabled EfficientNet B3 and Fine Tuning enabled ResNet 101, namely, TL-EN3 and FT-RN 101, to classify the severity of disease level of retinal fundus images. Initially, the preprocessing and augmentation processes are performed to bring out the clear view features of the raw fundus images. Then the segmentation phase constrains the whole region using the Chan-Vese algorithm. Twelve features are extracted and fed into the learning network for training purposes. The proposed work utilizes the TL-EN3 model to capture high-resolution patterns with high accuracy and integrates FT-RN 101 models to maintain a balance between efficiency and accuracy with fewer parameters. Experimental analysis is conducted with different metrics such as kappa coefficient (K-score), classification accuracy (CA), precision (P), recall (R), F1-measure (F1), and False Positive Rate (FPR) on three publically available datasets such as Kaggle, Messidor-1, and Messidor-2 datasets. Furthermore, some performance graphs are plotted for visualizing the architecture performance, including training loss, validation loss, training accuracy, and validation accuracy. The performance of the proposed FSSCM approach obtains high estimation values of 0.981 0.985 0.983, 0.98 0.986 0.984, and 0.98 0.985 0.98 in terms of P, R, and F1 on three datasets, respectively. Also, it achieves high estimation results of 99.02 0.993, 98.1 0.97, and 98.3 0.98 in terms of CA and K-score for three datasets, respectively. With a high training accuracy and a low level of training loss, the proposed method gets better severity level classification results than other models.

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Data availability

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Sachin Chavan, Nitin Choubey. The first draft of the manuscript was written by Sachin Chavan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Code availability

Not Applicable.

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  1. SVKM’S NMIMS, Mukesh Patel School of Technology Management and Engineering, Shirpur, Maharashtra, India

    Sachin Chavan & Nitin Choubey

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All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.

Conceptualization: Sachin Chavan; Methodology: Sachin Chavan, Nitin Choubey; Formal analysis and investigation: Sachin Chavan, Nitin Choubey; Writing - original draft preparation: Sachin Chavan; Writing - review and editing: Sachin Chavan; Supervision: Nitin Choubey.

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Correspondence to Sachin Chavan.

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Chavan, S., Choubey, N. An automated diabetic retinopathy of severity grade classification using transfer learning and fine-tuning for fundus images. Multimed Tools Appl 82, 36859–36884 (2023). https://doi.org/10.1007/s11042-023-15135-0

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