Version 1
: Received: 16 November 2023 / Approved: 17 November 2023 / Online: 17 November 2023 (15:32:16 CET)
How to cite:
Simović, A.; Lutovac-Banduka, M.; Davidović, K.; Lekić, S. Machine Learning Application to Brain Diseases Detection. Preprints2023, 2023111127. https://doi.org/10.20944/preprints202311.1127.v1
Simović, A.; Lutovac-Banduka, M.; Davidović, K.; Lekić, S. Machine Learning Application to Brain Diseases Detection. Preprints 2023, 2023111127. https://doi.org/10.20944/preprints202311.1127.v1
Simović, A.; Lutovac-Banduka, M.; Davidović, K.; Lekić, S. Machine Learning Application to Brain Diseases Detection. Preprints2023, 2023111127. https://doi.org/10.20944/preprints202311.1127.v1
APA Style
Simović, A., Lutovac-Banduka, M., Davidović, K., & Lekić, S. (2023). Machine Learning Application to Brain Diseases Detection. Preprints. https://doi.org/10.20944/preprints202311.1127.v1
Chicago/Turabian Style
Simović, A., Kristina Davidović and Snežana Lekić. 2023 "Machine Learning Application to Brain Diseases Detection" Preprints. https://doi.org/10.20944/preprints202311.1127.v1
Abstract
During a multi-detector computed tomography (MDCT) examination, it is crucial to efficiently organize, store, and transmit medical images in DICOM standard, which requires significant hardware resources and memory. Our project processed large amounts of DICOM images by classifying them based on cross-section views that may carry important information about a possible diagnosis. We ensured that images were retained and saved in PNG format to optimize hardware resources while preserving patient confidentiality. Furthermore, we have developed a graphical, user-friendly interface that allows physicians to visualize specific regions of interest in a patient's brain where changes may indicate disease. Our proposed method enables quick classification of medical images into predefined classes of confirmed diseases of brain parenchyma, contributing to swift decision-making for further diagnosis for more precisely evaluating and characterizing brain changes, and it can lead to the rapid application of adequate therapy, which may result in better outcomes.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
