A Two-Dimensional Convolutional Neural Network for Brain Tumor Detection From MRI

Aims: Cancerous brain tumors are among the most dangerous diseases that lower the quality of life of people for many years. Their detection in the early stages paves the way for the proper treatment. The present study aimed to present a two-dimensional Convolutional Neural Network (CNN) for detecting brain tumors under Magnetic Resonance Imaging (MRI) using the deep learning method. Methods & Materials: The proposed method has two stages of feature extraction and classification. A 12-layer CNN was used to extract the features of the MRI images and then the softmax activation function was used to classify these features. The proposed method was applied to a standard database consisting of three brain tumor types of meningioma, glioma, and pituitary. Findings: The proposed method had better performance compared to previously presented methods. Its accuracy was reported as 98.68%. Conclusion: Meningioma, glioma, and pituitary tumors are the most common types of brain tumors. Early detection of these tumors can decrease the risk of death. Because of its fully connected structure, the use of proposed deep CNN can help physicians to correctly detect brain tumors with MRI images.

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Segmentation of Brain Tumor Tissues in HGG and LGG MR Images Using 3D U-net Convolutional Neural Network

International Journal of Natural Computing Research ◽

10.4018/ijncr.2018040102 ◽

2018 ◽

Vol 7 (2) ◽

pp. 18-30 ◽

Cited By ~ 2

Author(s):

Poornachandra Sandur ◽

C. Naveena ◽

V.N. Manjunath Aradhya ◽

Nagasundara K. B.

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Brain Tumors ◽

Convolutional Neural Network ◽

Radiotherapy Planning ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Diagnosis And Prognosis ◽

Tumor Tissues ◽

Magnetic Resonance Imaging Mri

The quantitative assessment of tumor extent is necessary for surgical planning, as well as monitoring of tumor growth or shrinkage, and radiotherapy planning. For brain tumors, magnetic resonance imaging (MRI) is used as a standard for diagnosis and prognosis. Manually segmenting brain tumors from 3D MRI volumes is tedious and depends on inter and intra observer variability. In the clinical facilities, a reliable fully automatic brain tumor segmentation method is necessary for the accurate delineation of tumor sub regions. This article presents a 3D U-net Convolutional Neural Network for segmentation of a brain tumor. The proposed method achieves a mean dice score of 0.83, a specificity of 0.80 and a sensitivity of 0.81 for segmenting the whole tumor, and for the tumor core region a mean dice score of 0.76, a specificity of 0.79 and a sensitivity of 0.73. For the enhancing region, the mean dice score is 0.68, a specificity of 0.73 and a sensitivity of 0.77. From the experimental analysis, the proposed U-net model achieved considerably good results compared to the other segmentation models.

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A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/2392395 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Tahia Tazin ◽

Sraboni Sarker ◽

Punit Gupta ◽

Fozayel Ibn Ayaz ◽

Sumaia Islam ◽

...

Keyword(s):

Neural Network ◽

Brain Tumors ◽

Convolutional Neural Network ◽

Learning Strategy ◽

Severe Form ◽

Ultrasound Images ◽

X Ray ◽

Novel Approach ◽

Magnetic Resonance Imaging Mri ◽

Deep Feature Extraction

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients’ quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

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Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function

IEEE Access ◽

10.1109/access.2020.2993618 ◽

2020 ◽

Vol 8 ◽

pp. 89281-89290

Author(s):

Zhiguan Huang ◽

Xiaohao Du ◽

Liangming Chen ◽

Yuhe Li ◽

Mei Liu ◽

...

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Complex Networks ◽

Convolutional Neural Network ◽

Image Classification ◽

Activation Function

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Brain Tumor Classification using Deep Learning Algorithms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39280 ◽

2021 ◽

Vol 9 (12) ◽

pp. 417-426

Author(s):

Ankita Kadam

Keyword(s):

Neural Network ◽

Magnetic Resonance Imaging ◽

Neural Networks ◽

Deep Learning ◽

Brain Tumor ◽

Magnetic Resonance ◽

Brain Tumors ◽

Tumor Classification ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Mri

Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)

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Deep Convolutional Neural Network With a Multi-Scale Attention Feature Fusion Module for Segmentation of Multimodal Brain Tumor

Frontiers in Neuroscience ◽

10.3389/fnins.2021.782968 ◽

2021 ◽

Vol 15 ◽

Author(s):

Xueqin He ◽

Wenjie Xu ◽

Jane Yang ◽

Jianyao Mao ◽

Sifang Chen ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Brain Tumor ◽

Brain Tumors ◽

Convolutional Neural Network ◽

Feature Fusion ◽

Deep Convolutional Neural Networks ◽

Multi Scale ◽

Mri Brain ◽

Feature Information

As a non-invasive, low-cost medical imaging technology, magnetic resonance imaging (MRI) has become an important tool for brain tumor diagnosis. Many scholars have carried out some related researches on MRI brain tumor segmentation based on deep convolutional neural networks, and have achieved good performance. However, due to the large spatial and structural variability of brain tumors and low image contrast, the segmentation of MRI brain tumors is challenging. Deep convolutional neural networks often lead to the loss of low-level details as the network structure deepens, and they cannot effectively utilize the multi-scale feature information. Therefore, a deep convolutional neural network with a multi-scale attention feature fusion module (MAFF-ResUNet) is proposed to address them. The MAFF-ResUNet consists of a U-Net with residual connections and a MAFF module. The combination of residual connections and skip connections fully retain low-level detailed information and improve the global feature extraction capability of the encoding block. Besides, the MAFF module selectively extracts useful information from the multi-scale hybrid feature map based on the attention mechanism to optimize the features of each layer and makes full use of the complementary feature information of different scales. The experimental results on the BraTs 2019 MRI dataset show that the MAFF-ResUNet can learn the edge structure of brain tumors better and achieve high accuracy.

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AIU-Net: An Efficient Deep Convolutional Neural Network for Brain Tumor Segmentation

Mathematical Problems in Engineering ◽

10.1155/2021/7915706 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Yongchao Jiang ◽

Mingquan Ye ◽

Daobin Huang ◽

Xiaojie Lu

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Receptive Field ◽

Brain Tumors ◽

Convolutional Neural Network ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Segmentation Accuracy ◽

Spatial Pyramid Pooling ◽

Tumor Region

Automatic and accurate segmentation of brain tumors plays an important role in the diagnosis and treatment of brain tumors. In order to improve the accuracy of brain tumor segmentation, an improved multimodal MRI brain tumor segmentation algorithm based on U-net is proposed in this paper. In the original U-net, the contracting path uses the pooling layer to reduce the resolution of the feature image and increase the receptive field. In the expanding path, the up sampling is used to restore the size of the feature image. In this process, some details of the image will be lost, leading to low segmentation accuracy. This paper proposes an improved convolutional neural network named AIU-net (Atrous-Inception U-net). In the encoder of U-net, A-inception (Atrous-inception) module is introduced to replace the original convolution block. The A-inception module is an inception structure with atrous convolution, which increases the depth and width of the network and can expand the receptive field without adding additional parameters. In order to capture the multiscale features, the atrous spatial pyramid pooling module (ASPP) is introduced. The experimental results on the BraTS (the multimodal brain tumor segmentation challenge) dataset show that the dice score obtained by this method is 0.93 for the enhancing tumor region, 0.86 for the whole tumor region, and 0.92 for the tumor core region, and the segmentation accuracy is improved.

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Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v6i2.1219 ◽

2021 ◽

Author(s):

Agus Eko Minarno ◽

Mochammad Hazmi Cokro Mandiri ◽

Yuda Munarko ◽

Hariyady Hariyady

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Convolutional Neural Network ◽

Pituitary Tumors ◽

Model Testing ◽

Tumor Classification ◽

Appropriate Treatment ◽

Tumor Types ◽

The Brain ◽

Best Parameters

Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

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