A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

Akila Gurunathan,Batri Krishnan

doi:10.1007/s11682-021-00598-2

Akila Gurunathan, Batri Krishnan

Open Access

https://doi.org/10.1007/s11682-021-00598-2

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Abstract

A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occurring brain tumor in adults. The proposed CNN Deep Net classifier extracts the features internally from the enhanced image and classifies them into normal and abnormal tumor images. The segmentation of tumor region is performed by global thresholding along with an area morphological function. This proposed method of fully automated classification and segmentation of brain tumor preserves the spatial invariance and inheritance. Furthermore, based on its feature attributes the proposed CNN Deep net classifier, classifies the detected tumor image either as (low grade) benign or (high grade) malignant. This proposed CNN Deep net classification approach with grading system is evaluated both quantitatively and qualitatively. The quantitative measures such as sensitivity, specificity, accuracy, Dice similarity coefficient, precision, F-score of the proposed classifier states a better segmentation accuracy and classification rate of 99.4% and 99.5% with respect to ground truth images.

Highlights

A tumor is a volume of irregular and abnormal cells affecting the function of nearby healthy cells in a human body
The diagnosis of brain tumor begins with magnetic resonance imaging (MRI) as it provides detailed information on both hard and soft tissues with fat and fluid substances of the brain through electromagnetic fields when compared to other imaging modalities like CT, X ray, PET etc
Weiner filter is used for denoising with potential field clustering .Morphological segmentation is performed in FLAIR and T2 MRI images and features are extracted by fused LBP and GWT.The proposed model achieves 93% precision and 96% accuracy

Summary

Introduction

A tumor is a volume of irregular and abnormal cells affecting the function of nearby healthy cells in a human body. Malignant tumors are fast in their growth, and their rate of reoccurrence is high even after the surgery These tumors spread to other parts of the body through the lymphatic blood vessels. A typical Meningioma -Grade II occupies 22-26 percent of the occurrence in the total tumor occurrence percentage rate. They have a tumor cell growth rate of 15 mitoses per HPF. According to the reports published in Cancer.Net and WHO 2019, automated classifier architecture for the detection and classification of tumor is an emerging research area in the field of medicine. This provokes number of researchers to develop a cost effective as well as more precise automated algorithm for detection, classification and diagnosis of tumor

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