A Convolutional Deep Neural Network Based Brain TumorDiagnoses Using Clustered Image and Feature-Supported Classifier (CIFC)Technique.

Abstract

The recognizing and categorizing of a glioma brain tumor is a challenging task in the medical domain, and earlier identification of malignancy is much essential in order to increase the patient lifespan. Medical image analysis research has been performed to aid in the detection of malignant brain tumors. To achieve high classification performance, extracted features must be both descriptive and discriminatory. Machine learning is crucial in categorization due to its flexibility and adaptability to different problems. We have proposed a clustered image and feature-supported classifier (CIFC) along with a deep convolutional neural network framework in order to classify the brain tumor image. The proposed model consists of various classifiers such as; (i) original and segmented image feature-supported classifiers; (ii) original and segmented image-supported classifiers and (iii) clustered image and feature-supported classifiers. The free and open-access image dataset BRATS 2021 is used to train and test the proposed system framework for the tumor detection. The CFIC outperforms almost every classifier that has been proposed thus far. The performance metric outcome of the proposed system is 99.76% of sensitivity, 98.04% of specificity and 99.87% of accuracy significantly. Hence, the proposed system outcome performs well in terms of tumor detection when compared with other existing techniques.

Keywords:
Glioma brain tumour; Malignancy; Deep convolutional neural network; Clustered image and feature supported classifier; Image segmentation.

HIGHLIGHTS

• Medical image analysis research has been performed to aid in the detection of malignant brain tumors.

• OSFC, OSIC and CIFC performed well and produced better results in classification.

• The performance metric outcome of the proposed system is 99.76% of sensitivity, 98.04% of specificity and 99.87% of accuracy.