Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach

2021 ◽  
Author(s):  
Fatemh Bashir-Gonbadi ◽  
Hassan Khotanlou
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Tumor Classification ◽  
Convolutional Autoencoder

scholarly journals Brain Tumor Classification Using Convolution Neural Network

2021 ◽  
Vol 1916 (1) ◽  
pp. 012206
Author(s):  
N Saranya ◽  
D Karthika Renuka ◽  
J Nikkesh kanthan
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Tumor Classification ◽  
Convolution Neural Network

Brain Tumor Classification Using Convolutional Neural Network

2018 ◽  
pp. 183-189 ◽  
Author(s):  
Nyoman Abiwinanda ◽  
Muhammad Hanif ◽  
S. Tafwida Hesaputra ◽  
Astri Handayani ◽  
Tati Rajab Mengko
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Tumor Classification

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.

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