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

doi:10.34288/jri.v8i3.536

Authors

Muhammad Dimas Romero Yusuf Daywin Universitas Pembangunan Nasional Veteran Jakarta
Naufal Universitas Pembangunan Nasional Veteran Jakarta
Kevin Universitas Pembangunan Nasional Veteran Jakarta
Danendra Universitas Pembangunan Nasional Veteran Jakarta
Rangga Universitas Pembangunan Nasional Veteran Jakarta

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i3.536

Keywords:

Digital Image Processing, Classification, MRI, CNN Method, Brain Tumor

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.

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