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Identify glomeruli in human kidney tissue images using a deep learning approach


Healthcare is the most important need of today’s era. Healthcare refers to the improvement of the human health by preventing, curing, diagnosing, recovering from a health hazard caused. Thus, to improve the health condition of a human system technology, such as machine learning, deep learning and artificial intelligence has come into play. The combination of artificial technology with the health sector has made a huge impact and success on the world. Curing millions of diseases, analysis of various infections, providing accurate test results and high-level maintenance check are now all possible with the evolution of technology. Every part of human body can now be diagnosed and analyze to study all kinds of tissues, blood vessels, organs, cells for improvement of health and curing of diseases. Research sector has been working with a continuous pace to accomplish various studies to identify different body organs and have a descriptive study for the identification of proper working mechanism of the human body. One such study has also shown a huge progress in the recent times, the identification of glomeruli in human kidney tissue. The tiny ball like structured which is composed of blood vessels that has an actively participation in the filtration of the blood to form urine. Thus, the paper presents a deep learning-based model formed for the identification of these glomeruli present in the human kidney. After implementing, the proposed model obtained an accuracy of 99.68% with a dice coefficient of 0.9060.

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This research was financially supported by the Ministry of Education Malaysia under FRGS Grant (No.: FRGS/1/2018/STG06/UPM/02/6).

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All authors have equally contributed towards the formation of this paper.

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Correspondence to Ali Ahmadian.

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Communicated by Oscar Sanjuán Martínez.

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Shubham, S., Jain, N., Gupta, V. et al. Identify glomeruli in human kidney tissue images using a deep learning approach. Soft Comput 27, 2705–2716 (2023).

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  • Deep learning
  • Dice coefficient
  • EfficientNet
  • Human kidney
  • Image segmentation
  • PAS-stained kidney images