Mahmood, R.M.Ramaha, N.T.A.Karas, I.R.2024-09-292024-09-2920240094-243Xhttps://doi.org/10.1063/5.0212771https://hdl.handle.net/20.500.14619/95192022 Transdisciplinary Symposium on Engineering and Technology: Development of Digital and Green Technology on Post Pandemic Era, TSET 2022 -- 21 September 2022 -- Yogyakarta -- 201017Since a brain tumor is essentially a collection of aberrant tissues, it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification using machine learning from brain MRI scans are well-known to be challenging and important endeavors. Machine learning has the potential to be used in diagnostics, preoperative planning, and postoperative evaluations. Furthermore, it is crucial to get accurate measurements of the tumor's location on an MRI of the brain. The development of machine learning models and other technologies will let radiologists detect malignancies without having to cut into patients. Pre-processing, skull stripping, and tumor segmentation are the steps in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The methods' efficacy is measured by precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%. © 2024 Author(s).eninfo:eu-repo/semantics/closedAccessConference Object10.1063/5.02127712-s2.0-851990982211N/A3077