A Combined Framework for Medical Image Classification and Detection of Brain Abnormalities Utilizing K-Nearest Neighbors and an Enhanced Convolutional Neural Network

Mahdi Koohi
Additional contact information
Mahdi Koohi: STU (Tehran-Iran), Iran

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 4, 662-669

Abstract: The detection of abnormalities in medical images plays a pivotal role in early diagnosis and treatment. This paper presents a hybrid approach that combines K-Nearest Neighbors (KNN) and deep learning techniques to improve medical image classification and anomaly detection. The method applies KNN for classifying images such as MRI, CT, and X-ray scans, focusing on abnormality detection in brain images. By integrating KNN classifiers with feature extraction methods, the approach addresses challenges such as class imbalance and small datasets, resulting in improved detection accuracy. The effectiveness of the proposed method is demonstrated on a medical image dataset, showing significant improvements in both classification and anomaly detection tasks.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ijltemas.in/DigitalLibrary/Vol.14Issue4/662-669.pdf (application/pdf)
https://www.ijltemas.in/papers/volume-14-issue-4/662-669.html (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjb:journl:v:14:y:2025:i:4:p:662-669

Access Statistics for this article

International Journal of Latest Technology in Engineering, Management & Applied Science is currently edited by Dr. Pawan Verma

More articles in International Journal of Latest Technology in Engineering, Management & Applied Science from International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS)
Bibliographic data for series maintained by Dr. Pawan Verma ().