AI in Healthcare: Kidney Anomaly Detection from CT Scans

Author(s)	Ms. Sreepriya Madiwal, Ms. Ginikunta Suchitra, Ms. Arepally Shravya, Mr. Shaik Junaid, Dr. Battula Balnarsaiah, S.Ramachandra Reddy
Country	India
Abstract	Early detection and appropriate classification of such abnormalities in the kidney such as stones, cysts, and tumor is vital in order to treat them early. This paper presents a complete AI web application that would be the first to classify the Computed Tomography (CT) scan images of kidneys into one of the four types: Normal, Cyst, Stone, or Tumor. It relies on a Deep Learning (DL) using Convolution Neural Networks (CNN) framework with the MobileNetV2 architecture implemented on top of the transfer learning model to enable the Image Classification with a high level of accuracy even on the sparse medical databases. The consideration of scalability, usability, and best practices in the modern software can also be viewed as part of this developed system; as a whole, the creation of the given system will be another tremendous step towards the implementation of AI in everyday medical diagnostics.
Keywords	Kidney CT scans, CNN, Deep Learning, MobilenetV2, Image Classification
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-16
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.73982

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research