International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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 Normal-Cyst-Tumor and Stone Detection by Using ML & Deep Leaning

Author(s) Ms. VARSHARANI J SHEELAVANT, Prof. RATNAMALA PASWAN
Country India
Abstract Renal diseases present a major global health issue, making it essential to have accurate diagnostic tools to
enhance patient outcomes. This research focuses on this necessity by examining three primary types of
kidney diseases: stones, cysts, and tumours. Deep Learning (DL) detection algorithms have the potential
to enhance testing accuracy while decreasing diagnostic times, the workload for radiologists, and overall
costs. The study introduces Computational Intelligence with a Deep Learning Decision Support System
for Kidney Cancer (CIDLDSSKC) methodology applied to renal imaging. Timely diagnosis can greatly
improve the chances of a favorable prognosis for patients. Creating an artificial intelligence-driven system
to aid in the diagnosis of kidney cancer is vital, as kidney-related ailments are a worldwide health issue,
compounded by the scarcity of qualified nephrologists to assess kidney cancer.
Identifying and classifying various types of renal failure poses the greatest challenge in treating kidney
cancer. Renal Cell Carcinoma (RCC), which constitutes about 85% of adult kidney cancer cases, presents
substantial diagnostic difficulties. Timely detection is vital for successful prevention and treatment;
however, the manual evaluation of whole slide images (WSI) is labour-intensive and susceptible to
inconsistencies. To tackle these challenges, we propose a Computational Intelligence with Deep Learning
Based Decision Support System for Kidney Cancer (CIDLDSSKC). Renal Cell Carcinoma (RCC), which
makes up roughly 85% of adult cases, presents significant diagnostic obstacles. Early identification is
essential for effective prevention and treatment, but the manual analysis of whole slide images (WSI)
remains tedious and vulnerable to variability.
Keywords Acute Kidney Disease, Chronic Kidney Disease, Machine Learning, Python Programming, Deep Learning.
Field Biology
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.49865
Short DOI https://doi.org/g9r8dk
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