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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/33919358
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. 2021 Apr 21;11(5):744.
doi: 10.3390/diagnostics11050744.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

Momina Masood  1 , Tahira Nazir  1 , Marriam Nawaz  1 , Awais Mehmood  1 , Junaid Rashid  2 , Hyuk-Yoon Kwon  3 , Toqeer Mahmood  4 , Amir Hussain  5
Affiliations

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

Momina Masood et al. Diagnostics (Basel). .

Abstract

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

Keywords: MRI; Mask-RCNN; brain tumor; deep learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram of the proposed method.
Figure 2
Figure 2
Sample original images and corresponding ground truth masks.
Figure 3
Figure 3
The structure of the proposed technique.
Figure 4
Figure 4
DenseNet-41 architecture.
Figure 5
Figure 5
Pictorial representation of IOU.
Figure 6
Figure 6
Pictorial representation of precision.
Figure 7
Figure 7
Pictorial representation of recall.
Figure 8
Figure 8
Example segmentation results of high-score-obtaining test images using the proposed method. The red contour shows the predicted tumor mask.
Figure 9
Figure 9
Tumor localization results of the proposed approach over datasets using DenseNet-41 (a) Figshare, (b) Brain MRI dataset. + sign shows the outer value which is larger than the other values.
Figure 10
Figure 10
Example of inaccurately localized brain tumor images by the proposed method. The red and blue contour shows the predicted tumor region and respective masks.
Figure 11
Figure 11
Confusion matrix of the presented technique using DenseNet-41. (a) Figshare Brain Tumor Dataset, (b) Brain MRI Dataset.
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