scholarly journals Segmentation Techniques for Brain Tumor from MRI – A Survey

2020 ◽  
Author(s):  
Jeevitha R ◽  
Selvaraj D
Keyword(s):  
Brain Tumor ◽  
Medical Science ◽  
Radio Waves ◽  
Mri Scan ◽  
Generative Adversarial Network ◽  
Usual Procedure ◽  
Adversarial Network ◽  
Contrast Enhanced ◽  
Pros And Cons ◽  
Magnetic Resonance Imaging Mri

In the medical science, Biomedical images are the core. Generally, Magnetic Resonance Imaging(MRI) scan is the most usual procedure followed. Radio waves and strong magnetic flux were used to determine comprehensive images of tissues and organs inside the body. The enhancement in MRI scan has become a large milestone in the medical world. Generally, the brain is segmented into White and gray matter, and cerebrospinal fluid(CSF). Various segmentation techniques have been proposed with promising results. Still, they all have their own pros and cons. Deep neural networks(DNN) have established good performance in segmentation and classification task via Deep Wavelet Autoencoder(DWA). In this study, by using a pairwise Generative Adversarial Network(GAN) model, it addresses the problems in brain tumor detection using MRI from various scanner modalities T1 weighted, T2 weighted, T1 weighted with contrast-enhanced and FLAIR images.

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection.

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

scholarly journals Extricating Brain Tumor in MRI Images with the aid of MATLAB software

2021 ◽  
Vol 9 (VII) ◽  
pp. 502-506
Author(s):  
Shivam Kumar Mittal
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Image Quality ◽  
Brain Mri ◽  
Medical Science ◽  
Noise Removal ◽  
Mri Scan ◽  
Matlab Software ◽  
The Brain ◽  
Mri Image

In the current era of Medical Science, Image Processing is the most evolving and inspiring technique. This technique consolidates some noise removal functions, segmentation, and morphological activities which are the fundamental ideas of image processing. Initially preprocessing of an MRI image is done to ensure the image quality for further processing/output. Our paper portrays the methodology to extricate and diagnose the brain tumor with the help of an affected person’s MRI scan pictures of the brain. MRI pictures are taken into account to recognize and extricate the tumor from the brain with the aid of MATLAB software.

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Author(s):  
Dr Kumaravel A. ◽  
◽  
Jasmeena Tariq ◽  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

scholarly journals AdvAndMal: Adversarial Training for Android Malware Detection and Family Classification

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1081
Author(s):  
Chenyue Wang ◽  
Linlin Zhang ◽  
Kai Zhao ◽  
Xuhui Ding ◽  
Xusheng Wang
Keyword(s):  
Malware Detection ◽  
Generative Adversarial Network ◽  
Android Malware ◽  
Adversarial Network ◽  
Android Malware Detection ◽  
System Calls ◽  
Pros And Cons ◽  
Adversarial Training ◽  
Family Classification ◽  
Detection Technologies

In recent years, Android malware has continued to evolve against detection technologies, becoming more concealed and harmful, making it difficult for existing models to resist adversarial sample attacks. At the current stage, the detection result is no longer the only criterion for evaluating the pros and cons of the model with its algorithms, it is also vital to take the model’s defensive ability against adversarial samples into consideration. In this study, we propose a general framework named AdvAndMal, which consists of a two-layer network for adversarial training to generate adversarial samples and improve the effectiveness of the classifiers in Android malware detection and family classification. The adversarial sample generation layer is composed of a conditional generative adversarial network called pix2pix, which can generate malware variants to extend the classifiers’ training set, and the malware classification layer is trained by RGB image visualized from the sequence of system calls. To evaluate the adversarial training effect of the framework, we propose the robustness coefficient, a symmetric interval i = [−1, 1], and conduct controlled experiments on the dataset to measure the robustness of the overall framework for the adversarial training. Experimental results on 12 families with the largest number of samples in the Drebin dataset show that the accuracy of the overall framework is increased from 0.976 to 0.989, and its robustness coefficient is increased from 0.857 to 0.917, which proves the effectiveness of the adversarial training method.

scholarly journals Deep learning model for glioma, meningioma and pituitary classification

2021 ◽  
Vol 10 (1) ◽  
pp. 88
Author(s):  
Toqa A. Sadoon ◽  
Mohammed H. Ali
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Contrast Enhanced ◽  
Common Causes ◽  
The Difference ◽  
Magnetic Resonance Imaging Mri ◽  
Confusion Matrices

<p>One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data preprocessing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are; precision of 99.1% for pituitary, sensitivity of 98.7% for glioma, specificity of 99.1%, and accuracy of 99.1% for pituitary. The overall accuracy obtained is 96.1%.</p>

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