MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques

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BMC Medical Informatics and Decision Making(2023)

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Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency.

Abstract

Background Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages. Materials and Methods A dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study. Results The training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods ( p -value < 0.05). Conclusion The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection.

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques

Published in

BMC Medical Informatics and Decision Making(2023)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

References44 items

Efficient Deep Neural Networks for Classification of Alzheimer’s Disease and Mild Cognitive Impairment from Scalp EEG Recordings

Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI

Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio

Deep learning-based cardiovascular image diagnosis: A promising challenge

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

Exposure to lead increases the risk of meningioma and brain cancer: A meta-analysis.

A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow

Deep learning based enhanced tumor segmentation approach for MR brain images

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

Detection of Brain Tumor based on Features Fusion and Machine Learning

Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines

Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks

Brain Tumors.

Brain Tumor Classification Using Convolutional Neural Network

Dense and Diverse Capsule Networks: Making the Capsules Learn Better

Brain Tumor Type Classification via Capsule Networks

An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier

Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm

Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network

Overview of deep learning in medical imaging

A data mining study on combustion dynamics and NOx emission of a swirl stabilised combustor with secondary fuel injection

Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma

Deep Learning in Medical Image Analysis.

Deep learning for brain tumor classification

A survey on deep learning in medical image analysis

Brain tumor classification from multi-modality MRI using wavelets and machine learning

Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

Deep Convolutional Neural Network for Inverse Problems in Imaging

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks

An Introduction to Convolutional Neural Networks

Learning both Weights and Connections for Efficient Neural Network

A survey of feature selection and feature extraction techniques in machine learning

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

World Congress on Medical Physics and Biomedical Engineering 2018

Non-invasively Grading of Brain Tumor Through Noise Robust Textural and Intensity Based Features

Using Convolutional Neural Networks for Image Recognition By

Detection of Brain Tumor by using ANN

Publisher's Note

Oncotargets and Therapy Dovepress Computer-aided Classification of Lung Nodules on Computed Tomography Images via Deep Learning Technique

One-way ANOVA for three parameters of precision, recall, and F-measure in eight modeling meth-ods showed a statistically significant difference between the methods ( p -value < 0.001)

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