Investigation and Classification of MRI Brain Tumors Using Feature Extraction Technique

Hamid, Marwan A. A.; Khan, Najeed Ahmed

doi:10.1007/s40846-020-00510-1

Cited by 24 publications

(8 citation statements)

References 25 publications

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“…Popular feature extraction methods are divided into two basic methods: using CNN network architecture and not using CNN network architecture. The authors in studies [8][9][10][11][12] used traditional feature extraction methods, whereas studies [8][9][10][11] used benign and malignant tumor images as data. Alves et al [12] used data on inflammatory and brain tumors.…”

Section: Related Workmentioning

confidence: 99%

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Han Trong,

Nguyen Van,

Vu Dang

2023

Applied Bionics and Biomechanics

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Objective. To localize and distinguish between benign and malignant tumors on MRI. Method. This work proposes a high-performance method for brain tumor feature extraction using a combination of complex network and U-Net architecture. And then, the common machine-learning algorithms are used to discriminate between benign and malignant tumors. Experiments and Results. The dataset of brain MRI of a total of 230 brain tumor patients in which 77 high-grade glioma patients and 153 low-grade glioma patients were processed. The results of classifying benign and malignant tumors achieved an accuracy of 99.84%. Conclusion. The high accuracy of experiment results demonstrates that the use of the complex network and U-Net architecture can significantly improve the accuracy of brain tumor classification. This method could potentially be useful for clinicians in aiding diagnosis and treatment planning for brain tumor patients.

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Section: Related Workmentioning

confidence: 99%

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Han Trong,

Nguyen Van,

Vu Dang

2023

Applied Bionics and Biomechanics

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“…Another work focused on the classification and segmentation of tumors using pre-trained AlexNet, where features were extracted using the Gray-Level Co-Occurrence Matrix (GLCM) [24]. Other works include classification into different types of tumors using CNN [25][26][27][28][29], SVM [30], Graph cut [31], Recurrent Neural Network (RNN) [32,33], AlexNet transfer learning network of CNN [34], Deep Neural Network (DNN) [35][36][37], VGG-16, Inception V3 and ResNet50 [38], SVM and KNN [39], and CNN ensemble method [40].…”

Section: Related Workmentioning

confidence: 99%

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Wahlang

Maji

Saha

et al. 2022

Sensors

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Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.

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“…But PET works by detecting radiation from a radiotracer and injecting it into the human's organ suitably. On the other hand, MRI uses powerful magnets to create a magnetic field that can display an organ from all perspectives [11]. This device has two modes of operation: high field for high-quality images and low field for quick diagnosis.…”

Section: Data Acquisitionmentioning

confidence: 99%

MRI brain tumor early detection, classification and performance evaluation using KFCM and SVM

Reddy¹,

Ramashri

2022

ijhs

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A tumor-infected brain is a dreadful illness. It is an area in the brain caused by cell development irregularity. An infected brain area might be challenging to identify and categorize using the MR imaging approach. Images of human brain anatomy are resulted using various imaging methods. Strange brain compositions are difficult to detect using standard image processing methods. MRI differentiates and explains the human neurological design. This research proposed an analytical method for detecting brain tumors. As a result, the brain tumor early diagnosis technique is crucial for reducing mortality rates. We propose a computer-aided radiology system that will analyze brain tumors from MRI data to diagnosis. We constructed a model that uses FCM and Kernel FCM to segment the MRI images and DWT to extract features and the SVM network to classify tumors.

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Investigation and Classification of MRI Brain Tumors Using Feature Extraction Technique

Cited by 24 publications

References 25 publications

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

MRI brain tumor early detection, classification and performance evaluation using KFCM and SVM

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