Classification and Diagnosis of Meningioma Brain Tumors Using Centric Convolutional Neural Networks
Keywords:
brain, classifier, meningioma, segmentation, tumorsAbstract
The abrupt changes in brain cells creates tumor region in human brain. The detection of brain tumors on time saves the human life. Even though there are lots of brain tumors available, meningioma is most important which causes immediate death. Therefore, its detection is important at an earlier stage. The main objective of this article is to develop the meningioma brain tumor detection system using deep learning methods in MRI brain images. The conventional methods detected meningioma brain tumors with low tumor segmentation accuracy. Hence, there is a need for developing the meningioma detection system with high tumor segmentation accuracy. This article proposes a Centric based deep Convolutional Neural Network (CCNN) architecture for detecting the meningioma brain tumor images from the healthy case brain images. The proposed method uses Dual Tree Complex Wavelet (DTCWT) for decomposing the brain image and the features are derived from these decomposed sub bands. Further, these features are classified using CCNN classifier, which detects the meningioma brain image from the healthy brain images. The segmented tumor regions are computed from the classified meningioma image using morphological method and these segmented tumor regions are used to estimate the severity levels of the meningioma tumors using the proposed CCNN architecture. This proposed meningioma brain tumor detection approach using CCNN classifier model is tested on publicly available datasets Nanfang University (NU) and Kaggle. This proposed method obtains 98.93% Sensitivity Index Rate (SEIR), 99.02% Specificity Index Rate (SPIR), 99.16% Accuracy Rate (AR), 99.06% Precision Rate (PR) and 98.95% F1-Score (FS) for NU dataset images. This proposed method obtains 98.89% SEIR, 98.74% SPIR, 99.05% AR, 98.93% PR and 98.91% FS for Kaggle dataset images. From the quantitative analysis of the experimental analysis, it is concluded that the proposed CCNN method provides optimum results for meningioma brain tumor detection system with other similar state of the art methods.
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Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04. https://doi.org/10.17762/ijfrcsce.v8i2.2066
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