Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

Pham Van Hai,Samson Eloanyi Amaechi

doi:10.4018/ijcini.20211001.oa47

Abstract

Conventional methods used in brain tumors detection, diagnosis, and classification such as magnetic resonance imaging and computed tomography scanning technologies are unbridged in their results. This paper presents a proposed model combination, convolutional neural networks with fuzzy rules in the detection and classification of medical imaging such as healthy brain cell and tumors brain cells. This model contributes fully on the automatic classification and detection medical imaging such as brain tumors, heart diseases, breast cancers, HIV and FLU. The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance than the other current methods in the literature such as [classification of tumors in human brain MRI using wavelet and support vector machine 94.7%, and deep convolutional neural networks with transfer learning for automated brain image classification 95.0%], uses in the detection, diagnosis, and classification of medical imaging decision supports.

Highlights

The current World Health Organization (WHO) guidelines for brain tumor classification are strictly histopathological, which limits clinical application (Geethu Mohan et al.,2018)
The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance over the other current methods in the literature such as classification of tumors in human brain magnetic resonance imaging (MRI) using wavelet and support vector machine (94.7%) and deep convolutional neural networks with transfer learning for automated brain image classification (95.0%) used in the detection, diagnosis, and classification of medical imaging
A comparative analysis compared the performance of the current proposed model deep convolutional neural networks with transfer learning for automated brain image classification and classification of tumors in human brain MRI using wavelet and support vector machine methods

Summary

Introduction

The current World Health Organization (WHO) guidelines for brain tumor classification are strictly histopathological, which limits clinical application (Geethu Mohan et al.,2018). This has a constraint in the field of medical imaging for diagnosis and classification planning including automated approaches. Approaches have been abandoned in favor of non-invasive, high-resolution techniques, especially magnetic resonance imaging (MRI) and computed tomography (CT) scans (Thomas et al, 2018), though MRI is typically the reference standard used (Michael et al.,2018). Analyses of large-scale medical imaging data involving deep learning are rapidly evolving to include classification, detection, and diagnosis. Imaging brain tumor treatment approaches, such as pneumonia cephalography and cerebral angiography are invasive and potentially dangerous

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Conclusion