Classification of brain tissues as lesion or healthy by 3D convolutional neural networks

Aydogdu, Cem Yusuf; Albay, Enes; Ünal, Gözde

doi:10.1109/siu.2017.7960524

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…In view of this, we also intended to use depth. The neural network detects the area of cerebral small vessel lesions in the MRI image of the brain and seeks to find the features of the CNN structure and classify the ranking of the stroke and the location of the block [20,21].…”

Section: Related Workmentioning

confidence: 99%

Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning

Hsieh

Luo

Pan

et al. 2019

Sensors

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Magnetic resonance imaging (MRI) offers the most detailed brain structure image available today; it can identify tiny lesions or cerebral cortical abnormalities. The primary purpose of the procedure is to confirm whether there is structural variation that causes epilepsy, such as hippocampal sclerotherapy, local cerebral cortical dysplasia, and cavernous hemangioma. Cerebrovascular disease, the second most common factor of death in the world, is also the fourth leading cause of death in Taiwan, with cerebrovascular disease having the highest rate of stroke. Among the most common are large vascular atherosclerotic lesions, small vascular lesions, and cardiac emboli. The purpose of this thesis is to establish a computer-aided diagnosis system based on small blood vessel lesions in MRI images, using the method of Convolutional Neural Network and deep learning to analyze brain vascular occlusion by analyzing brain MRI images. Blocks can help clinicians more quickly determine the probability and severity of stroke in patients. We analyzed MRI data from 50 patients, including 30 patients with stroke, 17 patients with occlusion but no stroke, and 3 patients with dementia. This system mainly helps doctors find out whether there are cerebral small vessel lesions in the brain MRI images, and to output the found results into labeled images. The marked contents include the position coordinates of the small blood vessel blockage, the block range, the area size, and if it may cause a stroke. Finally, all the MRI images of the patient are synthesized, showing a 3D display of the small blood vessels in the brain to assist the doctor in making a diagnosis or to provide accurate lesion location for the patient.

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Section: Related Workmentioning

confidence: 99%

Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning

Hsieh

Luo

Pan

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The use of the Convolutional Neural Network (CNN) has been widely used without the use of additional feature extraction as in [3], the Sparse Autoencoder (SAE) method and the Convolutional Neural Network (CNN) train and PET-MRI combination to diagnose the patient's illness. In the study [4], CNN is also used to classify brain tissue affected by ischemic stools. Not only that, CNN is also used for the classification and segmentation of brain images based on the age of the BRATS dataset [5].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

Widhiarso¹,

Yohannes²,

Prakarsah³

2018

Indonesian J. Electron. Instrum. Syst.

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Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.

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Classification of brain tissues as lesion or healthy by 3D convolutional neural networks

Cited by 2 publications

References 4 publications

Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning

Cerebral Small Vessel Disease Biomarkers Detection on MRI-Sensor-Based Image and Deep Learning

Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

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