A framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm
01 Jan 2020-International Journal of Advanced Computer Science and Applications (The Science and Information (SAI) Organization Limited)-Vol. 11, Iss: 8
TL;DR: The method to detect a brain tumor and classification has been present and the tumorous brain MRI is classified using CNN based AlexNet architecture and the malignant brain tumor isclassified using GooLeNet transfer learning architecture.
Abstract: The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.
Citations
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TL;DR: In this paper, a robust U-Net deep learning Convolutional Neural Network (CNN) model was proposed to classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical grade application.
Abstract: Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.
10 citations
30 Oct 2022
3 citations
10 Sep 2022
TL;DR: In this article , the classification system uses transfer learning because only a few datasets are used and the pre-trained models used to extract features are VGG-16 and ResNet-50.
Abstract: One type of deadly disease is a brain tumor. To determine the presence of a brain tumor, it can be seen from an MRI image. In this research, we classified brain tumor MRI. The classification system uses transfer learning because only a few datasets are used. The Pre-Trained models used to extract features are VGG-16 and ResNet-50. Tests are carried out using several different parameters such as different batch sizes, optimizers, and learning rates. We evaluate the results using the confusion matrix. VGG-16 got the best accuracy of 0.96 using the Adam optimizer and ResNet-50 got the best accuracy of 0.94 using the RMSprop optimizer. From several different parameter variations, there is a relationship between parameter selection and accuracy results.
16 Oct 2022
TL;DR: In this article , a tuned ensemble learning approach is adopted in the early detection and severity analysis of brain cancer with reduced variance, and bagging algorithm has shown better performance with 98.4% accuracy comparatively.
Abstract: Brain cancer is an immoderate growth of cells in the brain that forms masses called tumors. Gliomas are the most prevalent and abrasive type of brain cancer, with a short life expectancy. The manifestation of Glioma is not clear yet and it can occur in people of all ages. The prevalence rate of central nervous system (CNS) tumors in India ranges between 5 and 10 per 100,000 people, which accounts for the tenth most common kind of cancer among Indians. Owing to the poor doctors to population ratio, there is a need for the fast and correct diagnosis of brain cancer from magnetic resonance imaging (MRI). In the present work, tuned ensemble learning approach is adopted in the early detection and severity analysis of brain cancer with reduced variance. The 2D MRI images of brain tumor with three types brain cancer was adopted in the study and bagging algorithm has shown better performance with 98.4% accuracy comparatively.
References
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05 Mar 2019
TL;DR: This paper proposed easy data augmentation techniques for boosting performance on text classification tasks, which consists of synonym replacement, random insertion, random swap, and random deletion, and showed that EDA improves performance for both convolutional and recurrent neural networks.
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789 citations
TL;DR: A computer‐assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis and consists of several steps including region‐of‐interest definition, feature extraction, feature selection, and classification.
Abstract: The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including ROI definition, feature extraction, feature selection and classification. The extracted features include tumor shape and intensity characteristics as well as rotation invariant texture features. Feature subset selection is performed using Support Vector Machines (SVMs) with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis (24), meningiomas (4), gliomas WHO grade 2 (22), gliomas WHO grade 3 (18), and glioblastomas (34). The binary SVM classification accuracy, sensitivity, and specificity, assessed by leave-one-out cross-validation, were respectively 85%, 87%, and 79% for discrimination of metastases from gliomas, and 88%, 85%, and 96% for discrimination of high grade (grade III and IV) from low grade (grade II) neoplasms. Multi-class classification was also performed via a one-versus-all voting scheme.
666 citations
TL;DR: A 3-class classification problem to differentiate among glioma, meningioma and pituitary tumors, which form three prominent types of brain tumor is focused on, which adopts the concept of deep transfer learning and uses a pre-trained GoogLeNet to extract features from brain MRI images.
576 citations
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...[14] presented a brain tumor classification technique using GoogLeNet....
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TL;DR: A block-wise fine-tuning strategy based on transfer learning is proposed that outperforms state-of-the-art classification on the CE-MRI dataset and can achieve average accuracy of 94.82% under five-fold cross-validation.
366 citations
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...It was training for classifying 1000 different objects [20]....
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TL;DR: This study proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images, and achieved 5-fold classification accuracy of 100% on 613 MR images.
339 citations
"A framework for Brain Tumor Segment..." refers background in this paper
...[12] suggested the deep transfer learning technique classifies MRI images of the brain as normal and abnormal....
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