A Novel Hybrid System Of Detecting Brain Tumors in Mri

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Agarwal, Raghav, Pande, Sagar Dhanraj, Mohanty, Sachi Nandan, Panda, Sandeep Kumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain modeling; BTs classification; Cancer; Classifiers; comparative analysis; Convolutional neural networks; Decision trees; deep CNN (DCNN); Deep learning; Diagnostic systems; Feature extraction; hybrid models; Hybrid systems; image processing; K-nearest neighbors algorithm; Machine learning; Magnetic resonance imaging; MRI images; Support vector machines; Transfer learning; Tumors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3326447

The growth of irregular brain cells leads to a disease called brain tumor (BT). It is difficult to predict a patient's chance of survival due to the low rate and wide range of tumor shapes. Even though it is possible to manually detect cancer, doing so is difficult and time-consuming and runs the risk of producing false-positive results. This can be done via MRI, which is necessary for locating cancer. It is very difficult to reliably identify different illnesses from MRI images for successful therapy via a computer-aided diagnostic system. In the experiment, three openly accessible benchmark datasets were utilized. To perform feature extraction in our proposed method, a CNN model was employed, followed by the application of five machine learning classifiers: Decision tree, Naive Bayes, Adaptive Boosting, K-nearest neighbor, and support vector machine. The outcomes show that the proposed CNN architecture with the KNN classifier performs better than previous CNN models by outperforming other cutting-edge DL models under various classification metrics. Finally, the achieved F1-Score, precision, recall, and accuracy values for the classification and detection of the proposed model were 99.58%, 99.59%, 99.58%, and 99.58%, respectively. For the comparison study, additional Transfer Learning models are utilized. Experimental findings support the strength of the proposed architecture, which has rapidly accelerated and improved the classifications of BTs. The designed method outperforms the body of existing knowledge, demonstrating that it is a quick and precise method for classifying BTs.