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All Journal ComEngApp : Computer Engineering and Applications Journal axial : jurnal rekayasa dan manajemen konstruksi Jurnal Nuansa Akademik: Jurnal Pembangunan Masyarakat Akademika : Jurnal Pemikiran Islam Jurnal Informatika Kesatuan
Nasrudin, Muhammad
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences

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Journal : ComEngApp : Computer Engineering and Applications Journal

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MRI-Based Brain Tumor Instance Segmentation Using Mask R-CNN Nasrudin, Muhammad
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.490

Abstract

Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor segmentation using Mask R-CNN, a state-of-the-art deep learning model for instance segmentation. Our method leverages MRI images to identify and delineate brain tumors with high precision. We trained the Mask R-CNN model on a dataset of annotated MRI images and evaluated its performance using the mean Average Precision (mAP) metric. The results demonstrate that our model achieves a high mAP of 90.3%, indicating its effectiveness in accurately segmenting brain tumors. This automated approach not only reduces the manual effort required for tumor segmentation but also provides consistent and reliable results, potentially improving clinical outcomes.
Co-Authors Ahmad Madkur Fadilah Fadilah, Fadilah Nugroho, Taufik Saefudin Zuhdi Siswoyo Siswoyo Suhendi Suhendi Syavicky, Ahmad Reihan Yurniati, Yurniati