Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

Vinayak Singh,Manoj Sahni,Manjusha Pandey,Harshvardhan GM,Luis F. Espinoza-Audelo,Ernesto Leon-Castro,Mahendra Kumar Gourisaria,Siddharth Swarup Rautaray

doi:10.3390/app12062900

Abstract

A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor.

Highlights

A brain tumor is a condition where there is a development of abnormal cells in the brain
Brain tumors can be categorized based on origin: primary brain tumors, which are cancer lesions that are apparent within the brain such as gliomas, oligodendrogliomas, pituitary adenomas, schwannomas, and astrocytomas whereas metastatic tumors develop at different locations of the CNS and migrate to the brain through arterial circulation [2]
This section is organized as follows: In 3.1 we have discuss main reason behind this research was to reduce the computational cost and provide an about the dataset used for this study, Section 3.2 explains about the image augmentation, efficient diagnosis system for brain tumors by using convolutional neural network (CNN)

Summary

Introduction

A brain tumor is a condition where there is a development of abnormal cells in the brain. When a brain tumor grows it increases the intracranial pressure which leads to brain damage and is even life-threatening. Brain tumors can be classified into two categories: benign and malignant tumors. Malignancies consist of cancer cells, whereas benign tumors consist of noncancerous cells. Brain tumors can be categorized based on origin: primary brain tumors, which are cancer lesions that are apparent within the brain such as gliomas, oligodendrogliomas, pituitary adenomas, schwannomas, and astrocytomas whereas metastatic tumors (secondary brain tumors) develop at different locations of the CNS and migrate to the brain through arterial circulation [2]. The frontal lobe is the most common site for metastatic tumors

Methods

Results

Conclusion