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Magma (New York, N.Y.)
Feb, 2025;38 (1): 13-22.

Brain tumor detection and segmentation using deep learning.

Rafia Ahsan, Iram Shahzadi, Faisal Najeeb, Hammad Omer
Author Information
  1. Rafia Ahsan: Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, Pakistan.
  2. Iram Shahzadi: OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine, University Hospital Carl Gustav Carus, Technische Universit��t Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
  3. Faisal Najeeb: Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, Pakistan. faisal.najeeb@comsats.edu.pk. ORCID
  4. Hammad Omer: Department of Electrical and Computer Engineering, Medical Image Processing Research Group (MIPRG), COMSATS University Islamabad, Islamabad, Pakistan.
PMID: 39231857 DOI: 10.1007/s10334-024-01203-5

Abstract

OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection algorithms on brain tumor data has not been widely explored. Therefore, we aim to compare different object detection algorithms (Faster R-CNN, YOLO & SSD) for brain tumor detection on MRI data. Furthermore, the best-performing detection network is paired with a 2D U-Net for pixel-wise segmentation of abnormal tumor cells.
MATERIALS AND METHODS: The proposed model was evaluated on the Brain Tumor Figshare (BTF) dataset, and the best-performing detection network cascaded with 2D U-Net for pixel-wise segmentation of tumors. The best-performing detection network was also fine-tuned on BRATS 2018 data to detect and classify the glioma tumor.
RESULTS: For the detection of three tumor types, YOLOv5 achieved the highest mAP of 89.5% on test data compared to other networks. For segmentation, YOLOv5 combined with 2D U-Net achieved a higher DSC compared to the 2D U-Net alone (DSC: YOLOv5���+���2D U-Net���=���88.1%; 2D U-Net���=���80.5%). The proposed method was compared with the existing detection and segmentation network i.e. Mask R-CNN and achieved a higher mAP (YOLOv5���+���2D U-Net���=���89.5%; Mask R-CNN���=���67%) and DSC (YOLOv5���+���2D U-Net���=���88.1%; Mask R-CNN���=���44.2%).
CONCLUSION: In this work, we propose a deep-learning-based method for multi-class tumor detection, classification and segmentation that combines YOLOv5 with 2D U-Net. The results show that the proposed method not only detects different types of brain tumors accurately but also delineates the tumor region precisely within the detected bounding box.

Keywords

Brain tumor Classification Deep learning Detection Magnetic resonance imaging (MRI) Segmentation

References

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MeSH Term

Deep Learning
Humans
Brain Neoplasms
Magnetic Resonance Imaging
Algorithms
Neural Networks, Computer
Glioma
Image Processing, Computer-Assisted
Image Interpretation, Computer-Assisted
Brain
Journal Article

Word Cloud

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