Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework
TL;DR: In this article, three different CNN models are proposed for three different classification tasks, i.e., classification of brain tumor MRI images using grid search optimization algorithm, which achieved 99.33% accuracy with the first CNN model and 92.66% with the second CNN model.
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Abstract: Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.
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Citations
Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model
Muhannad Alanazi,Muhammad Umair Ali,S. Javeed Hussain,Amad Zafar,Mohammed Mohatram,Muhammad Irfan,Raed Alruwaili,Mubarak Alruwaili,Naif H. Ali,Anas Mohammad Albarrak +9 more
TL;DR: A novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma is proposed.
Accurate brain tumor detection using deep convolutional neural network
TL;DR: Li et al. as mentioned in this paper proposed two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma and pituitary) brain tumors.
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A deep learning approach for brain tumor classification using MRI images
Muhammad Aamir,Zia-Uu Rahman,Zaheer Ahmed Dayo,Waheed Ahmed Abro,Muhammad Irfan Uddin,Inayat Khan,Ali Imran,Zafar Ali,Muhammad Ishfaq,Yurong Guan,Zhi-Guo Hu +10 more
TL;DR: In this article , the authors proposed an automated technique for detecting brain tumors using magnetic resonance imaging (MRI) images and applied two different pre-trained deep learning models to extract powerful features from images and combined them to form a hybrid feature vector using the partial least squares (PLS) method.
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A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models
TL;DR: In this article , a two-stage feature ensemble of deep convolutional neural networks (CNN) is proposed for precise and automatic classification of brain tumors, which achieved an average accuracy of 99.13%.
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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review
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Brain Tumor Classification Using Convolutional Neural Network
Nyoman Abiwinanda,Muhammad Hanif,S. Tafwida Hesaputra,Astri Handayani,Tati L. R. Mengko +4 more
- 01 Jan 2019
TL;DR: This study attempted to train a Convolutional Neural Network to recognize the three most common types of brain tumors, i.e. the Glioma, Meningiomas, and Pituitary, using the simplest possible architecture.
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Hybrid intelligent techniques for MRI brain images classification
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