Kevin Yosia
Universitas Pembangunan Nasional Veteran Jakarta

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DIGITAL IMAGE PROCESSING FOR BRAIN TUMOR CLASSIFICATION IN HUMANS USING CONVOLUTIONAL NEURAL NETWORKS Muhammad Dimas Romero Yusuf Daywin; Naufal Rasyad Muhammad; Kevin Yosia; Danendra Satya Purwoko; I Wayan Rangga Pinastawa
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.536

Abstract

The rapid development of digital technology has encouraged its utilization in various aspects of life, including the medical field. One significant application is digital image processing, which is used to enhance the quality and utility of medical imagery such as MRI and CT scans. This technology is highly relevant in diagnosing brain diseases, particularly brain tumors, which require high precision given the organ's complexity. This research focuses on the classification of brain tumor diseases using MRI images through the Convolutional Neural Network (CNN) method. CNN was selected due to its ability to extract essential features from MRI images, enabling it to identify complex patterns that are difficult for the human eye to recognize. With proper training, the CNN model is capable of distinguishing between healthy brain MRI images and those with tumors with an accuracy of 80%. These results demonstrate great potential in accelerating and improving the accuracy of the diagnostic process, which in turn assists in determining appropriate and effective treatment steps. This study provides a significant contribution to the development of medical diagnostic technology, specifically in brain tumor classification. Through the application of advanced digital image processing technology, it is expected that more efficient and accurate diagnostic tools can be created, thereby improving the quality of healthcare and patient treatment outcomes.