Enhancing Renal Diagnostic Accuracy with CNNs for Medical Image Classification

Authors

  • Chakka Balasruthi, Mallampati Rakesh Chowdary, Mamidi Mounika Author

DOI:

https://doi.org/10.48047/

Keywords:

.

Abstract

The accurate diagnosis of kidney anomalies, including cysts, stones, tumors, and normal tissues, plays a pivotal role in early intervention and effective treatment planning. However, traditional diagnostic methods, which rely heavily on the manual interpretation of radiological images such as CT scans, ultrasounds, and MRIs, present several challenges. These methods are not only time-intensive but are also prone to subjectivity, as they depend on the expertise and experience of radiologists, leading to inter-observer variability and potential misdiagnosis. Additionally, the exponential growth in medical imaging data due to advancements in scanning technology further overwhelms radiologists, making it increasingly difficult to maintain consistency and accuracy in diagnoses. 

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References

V. Jha, G. Garcia-Garcia, K. Iseki et al., “Chronic kidney disease: global dimension and perspectives,” The Lancet, vol. 382, no. 9888, pp. 260–272, 2013.

R. Ruiz-Arenas, “A summary of worldwide national activities in chronic kidney disease (CKD) testing, the electronic journal of the international federation of,” Clinical Chemistry and Laboratory Medicine, vol. 28, no. 4, pp. 302–314, 2017.

Thedailystar, “Over 35,000 develop kidney failure in Bangladesh every year,” 2019, https://www.thedailystar.net/city/news/18m-kidney-patients-bangladesh-every-year 1703665.

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Published

2021-09-05

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Articles

How to Cite

Chakka Balasruthi, Mallampati Rakesh Chowdary, Mamidi Mounika. (2021). Enhancing Renal Diagnostic Accuracy with CNNs for Medical Image Classification . History of Medicine, 7(2), 278-297. https://doi.org/10.48047/