Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface

2019 ◽  
Author(s):  
Hasan Ucuzal ◽  
Seyma YASAR ◽  
Cemil Colak
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Magnetic Resonance Images ◽  
Web Based ◽  
Tumor Types

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1171-1179
Author(s):  
Swaraja Kuraparthi ◽  
Madhavi K. Reddy ◽  
C.N. Sujatha ◽  
Himabindu Valiveti ◽  
Chaitanya Duggineni ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Data Augmentation ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

scholarly journals Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shota Ito ◽  
Yuichi Mine ◽  
Yuki Yoshimi ◽  
Saori Takeda ◽  
Akari Tanaka ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Temporomandibular Joint ◽  
Temporomandibular Disorders ◽  
Temporomandibular Disorder ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Articular Disc

AbstractTemporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

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