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CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images

CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images

Anil Kumar Mandle, Satya Prakash Sahu, Govind P. Gupta
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 20
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.304438
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MLA

Mandle, Anil Kumar, et al. "CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images." IJSSCI vol.14, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSSCI.304438

APA

Mandle, A. K., Sahu, S. P., & Gupta, G. P. (2022). CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-20. http://doi.org/10.4018/IJSSCI.304438

Chicago

Mandle, Anil Kumar, Satya Prakash Sahu, and Govind P. Gupta. "CNN-Based Deep Learning Technique for the Brain Tumor Identification and Classification in MRI Images," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-20. http://doi.org/10.4018/IJSSCI.304438

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Abstract

A brain tumor is an abnormal development of cells in the brain that are either benign or malignant. Magnetic resonance imaging (MRI) is used to identify tumors. Manual evaluation of brain tumors from MRI images by a radiologist is a challenging task. Hence, this paper proposes VGG-19 Convolutional Neural Networks (CNN)-based deep learning model for the classification of brain tumors. Initially, in the proposed model, contrast stretching technique is employed for noises removal. Next, a deep neural network is employed for rich feature extract. Further, these learning features are combined with classifier models of CNN for training and validation. performance analysis of the proposed methodology and experiments have been carried out using publicly available MRI images in Figshare dataset of 3064 slices from 233 subjects. The proposed model has achieved 99.83% accuracy. Moreover, the proposed model obtained precision 96.32%, 98.26%, and 98.56%, recall of 97.82%, 98.62%, 98.87%, and specificity of 98.72%, 99.51%, and 99.43% for the Glioma, Meningioma, and Pituitary tumors respectively.

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