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. 2022 Jun 21:2022:1830010.
doi: 10.1155/2022/1830010. eCollection 2022.

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Pallavi Tiwari  1 , Bhaskar Pant  1 , Mahmoud M Elarabawy  2 , Mohammed Abd-Elnaby  3 , Noor Mohd  1 , Gaurav Dhiman  1 , Subhash Sharma  4
Affiliations

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Pallavi Tiwari et al. Comput Intell Neurosci. .

Abstract

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Sample brain MRI from 4 different classes.
Figure 2
Figure 2
Training dataset distribution among 4 classes.
Figure 3
Figure 3
Testing dataset distribution among 4 classes.
Figure 4
Figure 4
Training accuracy of the proposed model.
Figure 5
Figure 5
Training loss of the proposed model.
Figure 6
Figure 6
Prediction result.
Figure 7
Figure 7
Confusion matrix of proposed model.
Figure 8
Figure 8
Classification report of the proposed model.
See this image and copyright information in PMC

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