5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

Farhi, Lubna; Zia, Razia; Ali, Zain Anwar

doi:10.33317/ssurj.v1i1.36

Cited by 8 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, classification has been carried out on other image databases, which are also quite small [15][16][17][18]. Mohsen et al used 66 images to classify four types of images showing brain tumors: tumor-free, glioblastoma, sarcoma, and metastasis.…”

Section: Introductionmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

show abstract

Section: Introductionmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

show abstract

“…In order to correctly diagnose the tumor and evade an unnecessary medical procedure and subjectivity, it is essential to build up a viable diagnostic tool for tumor characterization [20]. The Brain Tumor Segmentation Challenge (BRATS) [22] is still in progress, as reported in the literature [23][24][25][26][27][28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Development of the Tumor Diagnosis Application for Medical Practitioners using Transfer Learning

Sarwar

Noreen²,

Irshad³

2022

BioSci Rev.

View full text Add to dashboard Cite

A brain tumor is the growth of abnormal cells in the tissues of brain. It affects a large number of people of different ages, worldwide. Magnetic Resonance Imaging (MRI) is the most operative and widely used technique for brain tumor detection because it provides better contrast images of the brain. However, the complexity of the problem, manual classification process, requirement of skilled medical practitioners, and a huge amount of MRI scan data are the major factors thwarting the timely classification of tumor vs. non-tumor. Early detection of brain tumors is possible by accurately applying machine learning with the aim to save time, cost, and human life. Recently, deep machine learning via transfer learning techniques was found to be highly effective for classification tasks. A tumor diagnosis application is presented with a VGG-19-based deep learning model by applying transfer learning of knowledge. Five-fold cross-validation of the model demonstrated 88% accuracy along with a 0.881 F1 score. The application could be utilized as a successful tool aid for oncologists and radiologists in the clinical diagnostics process.

show abstract

Measure the superior functionality of machine intelligence in brain tumor disease prediction

Yadav¹,

Pal²

2022

Artificial Intelligence-Based Brain-Computer Interface

View full text Add to dashboard Cite

5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

Cited by 8 publications

References 2 publications

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

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

Development of the Tumor Diagnosis Application for Medical Practitioners using Transfer Learning

Measure the superior functionality of machine intelligence in brain tumor disease prediction

Contact Info

Product

Resources

About