Journal ArticleDOI
Deep CNN for Brain Tumor Classification
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TLDR
In this paper, the authors proposed a new model, which contains various layers in the aim to classify MRI brain tumor, and the proposed model is experimentally evaluated on three datasets.Abstract:
Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods.read more
Citations
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Journal ArticleDOI
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.
Journal ArticleDOI
Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images
Chetana Srinivas,N. K S,Mohammed Zakariah,Yousef Ajmi Alothaibi,Kamran Shaukat,B. Partibane,Halifa Awal +6 more
TL;DR: This work presents a comparative performance analysis of transfer learning-based CNN-pretrained VGG-16, ResNet-50, and Inception-v3 models for automatic prediction of tumor cells in the brain and estimates that the pretrained model V GG-16 determines highly adequate results with an increase in the accuracy rate of training and validation.
Journal ArticleDOI
Brain tumor detection and classification using machine learning: a comprehensive survey
Javaria Amin,Javaria Amin,Muhammad Sharif,Anandakumar Haldorai,Mussarat Yasmin,Ramesh Sundar Nayak +5 more
TL;DR: A comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers is presented in this paper, which covers the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumor analysis.
Journal ArticleDOI
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%.
Journal ArticleDOI
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.
References
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
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A survey on deep learning in medical image analysis
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