scholarly journals Combining Wavelet Statistical Texture And Recurrent Neural Network For Tumour Detection And Classification Over MRI

2019 ◽  
Vol 8 (6) ◽  
pp. 3769-3778
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Recurrent Neural Network ◽  
Detection System ◽  
Region Growing ◽  
Texture Features ◽  
Noise Removal ◽  
The Body ◽  
Mr Images ◽  
The Brain

Brain tumor is one of the major causes of death among other types of the cancer because Brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper we have introduced brain tumor detection system based on combining wavelet statistical texture features and recurrent neural network (RNN). Basically, the system consists of four phases such as (i) feature extraction (ii) feature selection (iii) classification and (iii) segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on sparse principle component analysis (SPCA) approach. The next step is to classify the brain image using Recurrent Neural Network (RNN). After classification, proposed system extracts tumor region from MRI images using modified region growing segmentation algorithm (MRG). This technique has been tested against the datasets of different patients received from muthu neuro center hospital. The experimentation result proves that the proposed system achieves the better result compared to the existing approaches

scholarly journals Brain Tumor Detection using Enhanced Convolution Neural Network for MR Images

2021 ◽  
Vol 10 (2) ◽  
pp. 10-13
Author(s):  
M.B. Bramarambika ◽  
◽  
M Sesha Shayee ◽  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Detection System ◽  
Human Life ◽  
Classification Problem ◽  
Tumor Detection ◽  
Convolution Neural Network ◽  
Brain Images ◽  
The Brain

Brain tumor is a mass that grows unevenly in the brain and directly affects human life. The mass occurs spontaneously because of the tissues surrounding the brain or the skull. There are two types of Brain tumor such as Benign and Malignant. Malignant brain tumors contain cancer cells and grow quickly and spread through to other brain and spine regions as well. Accurate and prompt diagnosis of brain tumors is essential for implementing an effective treatment of this disease. Brain images produced by the Magnetic Resonance Imaging (MRI) technique are a rich source of data for brain tumor diagnosis and treatment in the medical field. Due to the existence of a large number of features compared to the other imaging types. The performance of existing methods is inadequate considering the medical significance of the classification problem. Earlier methods relied on manually delineated tumor regions, prior to classification. This prevented them from being fully automated. The automatic algorithms developed using CNN and its variants could not achieve an influential improvement in performance. In order to overcome such an issue, the proposed one is automatic brain tumor detection system, which is “ Enhanced Convolution Neural Network (CNN) Algorithm for MRI Images” for the detection of brain tumor is useful to detect and classify the Glioma part into low Glioma and high Glioma.

scholarly journals Detection of Various Neoplasm’s in Medical Images Using Edge Detection and Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 59-62
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Detection System ◽  
Probabilistic Neural Network ◽  
The Body ◽  
Internal Cavity ◽  
Cell Detection ◽  
The People ◽  
Abnormal Cells ◽  
Occurrence Matrix

Brain tumor is one in all the extraordinary illness causes death among the people. Neoplasm is associate unconfined expansion of tissue in any neighborhood of the body. During the process have a tendency to tend to stand live taking man photos as input; resonance imaging that is guided into internal cavity of brain and offers the entire image of brain. In this paper brain tumor detection system is proposed. Here bunch methodology supported intensity was enforced. The Probabilistic Neural Network square measure used to identify the various levels of tumor like Malignant, Benign or traditional. PNN with Radial Basis are used for classification and segmentation of cells. In order to classify the normal or abnormal cells, proper decision need to be taken. This could be done in 2 levels: Gray-Level Co-occurrence Matrix and the classification are performed based on Neural Networks. The tumor cell detection is manually performed by the schematic methodology for X-radiation.

scholarly journals Brain Tumor Detection and Classification in Magnetic Resonance Imaging (MRI) using Region Growing, Fuzzy Symmetric Measure, and Artificial Neural Network Backpropagation

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Lugina Muhammad ◽  
Retno Novi Dayawanti ◽  
Rita Rismala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Region Growing ◽  
Seed Point ◽  
Cad System ◽  
Average Accuracy ◽  
Symmetric Measure ◽  
The Brain

<p>Brain tumor is one type of malignant tumors that occurs because there is an abnormal and uncontrolled cell division activity. There are several ways to diagnose brain tumors, for example use MRI images. Through the MRI images, the radiologist can see the brain anatomy without performing surgery. However, this process is still done manually and could lead to misdiagnose. In addition, the different characteristics of brain tumor makes the diagnose more difficult. Therefore, we need a system of Computer-Aided Diagnostic (CAD) that will help radiologist in identifying brain tumors. </p><p>In general, the CAD system consists of two major processes, namely image segmentation and feature extraction and classification. One example of segmentation is Region Growing that will classify the pixels based on certain criteria. However, the manual selection of seed point is a drawback of this method. The examples of feature extraction methods are Fuzzy Symmetric Measure (FSM), and First and Second Order Statistics. FSM values can be used to calculate the symmetry of the image brain, while the first and second order to represent feature in the image. As for the classification process, Artificial Neural Network Backpropagation method is widely used for its ability to resolve nonlinear dan complex problems.</p><p>This research implements CAD system that uses Region Growing, Symmetric Fuzzy Measure, and Backpropagation Neural Network for detecting and classifying the brain tumors. In addition, the modification of converging square is conducted to select a seed point automatically. After testing, the system generates a 100% accuracy and BER is 0 in the case of distinguishing between normal and tumor brain. Besides, the average accuracy in classifying the types of brain tumors achieved 89.72% , the BER 0.1 for training data, and the average accuracy of 84.44%, BER 0.16 for the testing data.</p>

scholarly journals Brain Tumor Detection System

2021 ◽  
Vol 9 (9) ◽  
pp. 1903-1917
Author(s):  
Prof. A S Salavi (Kumbhar)
Keyword(s):  
Brain Tumor ◽  
Quantitative Measurement ◽  
Clinical Medicine ◽  
Detection System ◽  
Automatic Segmentation ◽  
Complex Structure ◽  
Mr Images ◽  
Abnormal Growth ◽  
Tumor Diagnostics ◽  
The Brain

Abstract: Brain tumor is an abnormal growth of brain cells within the brain. Detection of brain tumor is a challenging problem, due to complex structure of the brain. The automatic segmentation has great potential in clinical medicine by freeing physicians from the burden of manual labeling; whereas only a quantitative measurement allows to track and modeling precisely the disease. Magnetic resonance (MR) images are an awfully valuable tool to determine the tumor growth in brain. But, accurate brain image segmentation is a complicated and time consuming process. MR is generally more sensitive in detecting brain abnormalities during the early stages of disease, and is excellent in early detection of cases of cerebral infarction, brain tumors, or infections. So, in this project we put forward a method for automatic brain tumor diagnostics using MR images. The proposed system identifies and segments the tumor portions of the images successfully. Keywords: MR, 2D Image, BrainTumor

Advanced Brain Tumor Detection System

2020 ◽  
Vol 3 (2) ◽  
pp. 31-45
Author(s):  
Monica S. Kumar ◽  
Swathi K. Bhat ◽  
Vaishali R. Thakare
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Language Processing ◽  
Detection System ◽  
The Body ◽  
Tumor Segmentation ◽  
Cancer Type ◽  
Brain Tumor Segmentation ◽  
Secondary Tumor ◽  
Tumor Region

Brain tumor segmentation and detection is one of the most critical parts in the field of medical regions. Tumor is a cancer type that can be visible in any part of the body in case of primary and secondary tumor. The different type of brain tumor is glioma, benign, malignant, meningioma. This research helps in retrieving the tumor region in the brain with the help of 2D MRI images. The system predicts using MATLAB which is a programming platform and analyze the tumor from different method like canny edge, Otsu's binary, fuzzy c-means (FCM), and k-means clustering to improve the borders using the pixel technique. Using convolution neural network (CNN), neural network, and natural language processing, the system detects brain tumor based on the pre-processing and post-processing feature. Moreover, the authors figure out which tumor affected is the most important feature to protect the lifespan in the initial stages. Finally, it acknowledges the result in the mail format to the doctor or patient.

Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

2019 ◽  
Vol 28 (4) ◽  
pp. 571-588 ◽  
Author(s):  
Srinivasalu Preethi ◽  
Palaniappan Aishwarya
Keyword(s):  
Brain Tumor ◽  
Classification Scheme ◽  
Texture Features ◽  
The Body ◽  
Tumor Classification ◽  
Flower Pollination Algorithm ◽  
Fuzzy C Means Clustering ◽  
Tumor Region ◽  
Occurrence Matrix ◽  
The Brain

Abstract A brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body. Proper and timely diagnosis can prolong the life of a person to some extent. Consequently, in this paper, we have proposed a brain tumor classification scheme on the basis of combining wavelet texture features and deep neural networks (DNNs). Normally, the system comprises four modules: (i) feature extraction, (ii) feature selection, (iii) tumor classification, and (iv) segmentation. Primarily, we eliminate the noise from the image. Then, the feature matrix is produced by combining wavelet texture features [gray-level co-occurrence matrix (GLCM)+wavelet GLCM]. Following that, we select the relevant features with the help of the oppositional flower pollination algorithm (OFPA) because a high number of features are major obstacles for classification. Then, we categorize the brain image based on the selected features using the DNN. After the classification procedure, the projected scheme extracts the tumor region from the tumor images with the help of the possibilistic fuzzy c-means clustering (PFCM) algorithm. The experimentation results show that the proposed system attains the better result associated with the available methods.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals Magnetic Resonace–Based Attenuation Correction for Micro–Single-Photon Emission Computed Tomography

2012 ◽  
Vol 11 (2) ◽  
pp. 7290.2011.00036 ◽  
Author(s):  
Vincent Keereman ◽  
Yves Fierens ◽  
Christian Vanhove ◽  
Tony Lahoutte ◽  
Stefaan Vandenberghe
Keyword(s):  
Attenuation Correction ◽  
Single Photon ◽  
Photon Emission ◽  
The Body ◽  
Emission Computed Tomography ◽  
Micro Ct ◽  
Mr Images ◽  
Brain Scans ◽  
The Brain ◽  
Photon Emission Computed Tomography

Attenuation correction is necessary for quantification in micro–single-photon emission computed tomography (micro-SPECT). In general, this is done based on micro–computed tomographic (micro-CT) images. Derivation of the attenuation map from magnetic resonance (MR) images is difficult because bone and lung are invisible in conventional MR images and hence indistinguishable from air. An ultrashort echo time (UTE) sequence yields signal in bone and lungs. Micro-SPECT, micro-CT, and MR images of 18 rats were acquired. Different tracers were used: hexamethylpropyleneamine oxime (brain), dimercaptosuccinic acid (kidney), colloids (liver and spleen), and macroaggregated albumin (lung). The micro-SPECT images were reconstructed without attenuation correction, with micro-CT-based attenuation maps, and with three MR-based attenuation maps: uniform, non-UTE-MR based (air, soft tissue), and UTE-MR based (air, lung, soft tissue, bone). The average difference with the micro-CT-based reconstruction was calculated. The UTE-MR-based attenuation correction performed best, with average errors ≤ 8% in the brain scans and ≤ 3% in the body scans. It yields nonsignificant differences for the body scans. The uniform map yields errors of ≤ 6% in the body scans. No attenuation correction yields errors ≥ 15% in the brain scans and ≥ 25% in the body scans. Attenuation correction should always be performed for quantification. The feasibility of MR-based attenuation correction was shown. When accurate quantification is necessary, a UTE-MR-based attenuation correction should be used.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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