Brain tumor detection and segmentation using deep learning

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 exp...

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Bibliographic Details
Published in:Magma (New York, N.Y.) Vol. 38; no. 1; pp. 13 - 22
Main Authors: Ahsan, Rafia, Shahzadi, Iram, Najeeb, Faisal, Omer, Hammad
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.02.2025
Subjects:
Algorithms
Basic Science - Image analysis
Biomedical Engineering and Bioengineering
Brain - diagnostic imaging
Brain Neoplasms - diagnostic imaging
Computer Appl. in Life Sciences
Databases, Factual
Deep Learning
Glioma - diagnostic imaging
Health Informatics
Humans
Image Interpretation, Computer-Assisted - methods
Image Processing, Computer-Assisted - methods
Imaging
Magnetic Resonance Imaging
Medicine
Medicine & Public Health
Neural Networks, Computer
Radiology
Research Article
Solid State Physics
Brain tumor
Deep learning
Magnetic resonance imaging (MRI)
Segmentation
Detection
Classification
ISSN:1352-8661, 1352-8661
Online Access:Get full text
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Summary: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.
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ISSN:1352-8661
1352-8661
DOI:10.1007/s10334-024-01203-5