Automatic identification of glomerular in whole-slide images using a modified UNet model

Kaur, Gurjinder and Garg, Meenu and Gupta, Sheifali and Juneja, Sapna and Rashid, Junaid and Gupta, Deepali and Shah, Asadullah and Shaikh, Asadullah (2023) Automatic identification of glomerular in whole-slide images using a modified UNet model. Diagnostics, 13 (19). pp. 1-14. E-ISSN 2075-4418

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Abstract

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model’s capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model’s superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.

Item Type:	Article (Journal)
Uncontrolled Keywords:	deep learning; detection; glomerular; kidney tissue; UNet; whole-slide images
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof Asadullah Shah
Date Deposited:	10 Oct 2023 14:49
Last Modified:	26 Oct 2023 16:48
URI:	http://irep.iium.edu.my/id/eprint/107400

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