Security Challenges and Application for Underwater Wireless Sensor Network

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

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Abnormality Detection in Brain CT Image Using Support Vector Machine

10.29007/jsfg ◽

2018 ◽

Author(s):

Bhavna Sharma ◽

Priyanka Mitra

Keyword(s):

Support Vector Machine ◽

Ct Images ◽

Kernel Functions ◽

Support Vector ◽

Svm Classifier ◽

Second Phase ◽

Normal Brain ◽

Brain Images ◽

Brain Ct ◽

Automated Method

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Brain Tumor Segmentation from Multimodal MRI Data based on GLCM and SVM Classifier

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.20211001oa02 ◽

2021 ◽

Vol 15 (4) ◽

pp. 0-0

Keyword(s):

Brain Tumor ◽

Surgical Navigation ◽

Disease Diagnosis ◽

Support Vector ◽

Tumor Segmentation ◽

Svm Classifier ◽

Normal Brain ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Normal Tissues

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

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Brain tumor segmentation from multimodal magnetic resonance imaging data based on gray-level co-occurrence matrix (GLCM) and an ensemble Support Vector Machine (SVM) classifier

10.21203/rs.3.rs-49212/v1 ◽

2020 ◽

Author(s):

Na Li ◽

Zheng Yang

Keyword(s):

Brain Tumors ◽

Support Vector ◽

Tumor Segmentation ◽

Svm Classifier ◽

Gray Level ◽

Resonance Imaging ◽

Dice Coefficient ◽

Brain Tumor Segmentation ◽

Brain Images ◽

Occurrence Matrix

Abstract Background Brain tumors, abnormal cells growing in the human brain,are common neurological diseases that are extremely harmful to human health. Malignant brain tumors can lead to high mortality. Magnetic resonance imaging (MRI)༌a typical noninvasive imaging technology, can produce high-quality brain images without damage and skull artifacts, as well as provide comprehensive information to facilitate the diagnosis and treatment of brain tumors. Additionally༌the segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. Methods We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure. We focus on the effects of GLCM texture on brain tumor segmentation. First, 112 GLCM features for each voxel were extracted. Next, these features were ranked using the SVM-recursive feature elimination (SVM-RFE) method. Based on the sorting results, we found that when the number of features was 60, the value of the Dice similarity coefficient (DSC) tended to be flat. The GLCM texture features maximal correlation coefficient, information measure of correlation, Angular Second Moment, sum of squares, difference variance, contrast, and inverse difference moment were important for segmentation. Finally, we selected the top 60 grayscale features and constructed an ensemble SVM classifier to separate the abnormal mass of tissue from normal brain tissues. Results The experimental material was a dataset called BraTs2015. The proposed model was verified with the Dice coefficient. For low-grade tumors, we obtained a 91.2% average Dice coefficient for segmenting the complete tumor region. For high-grade tumors, the average was slightly higher at 92.4%. Conclusion Our results demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

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A simplified framework for the detection of intracranial hemorrhage in CT brain images using deep learning

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210218100641 ◽

2021 ◽

Vol 17 ◽

Author(s):

Praveen K ◽

Sasikala M ◽

Janani A ◽

Nijisha Shajil ◽

Hari Nishanthi V

Keyword(s):

Deep Learning ◽

Intracranial Hemorrhage ◽

Feature Selection Method ◽

Ct Images ◽

Support Vector ◽

Svm Classifier ◽

Brain Images ◽

Ct Brain ◽

Ct Brain Images

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is utmost important to avoid untoward incidents that may even lead to death.Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Methods: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method, secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored and finally, a feature selection method, Principal Component Analysis (PCA) has been introduced in the AlexNet-SVM classifier model and its efficacy is explored.These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. Results: The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86%, sensitivity and specificity of 0.9986 for the detection of ICH in brain CT images. Conclusion: This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. Also, the proposed simplified deep learning framework manifests its ability as a screening tool to assist the radiological trainees for the accurate detection of ICH.

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A Kernel-Based Approach for Biomedical Named Entity Recognition

The Scientific World JOURNAL ◽

10.1155/2013/950796 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Rakesh Patra ◽

Sujan Kumar Saha

Keyword(s):

Kernel Function ◽

Text Processing ◽

Named Entity Recognition ◽

Kernel Functions ◽

Entity Recognition ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Named Entity ◽

Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

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A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w3-281-2014 ◽

2014 ◽

Vol XL-2/W3 ◽

pp. 281-285 ◽

Cited By ~ 21

Author(s):

B. Yekkehkhany ◽

A. Safari ◽

S. Homayouni ◽

M. Hasanlou

Keyword(s):

Kernel Function ◽

Temporal Integration ◽

Experimental Tests ◽

Kernel Functions ◽

Polynomial Kernel ◽

Support Vector ◽

Svm Classifier ◽

Rbf Kernel ◽

Multi Temporal ◽

Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

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An Automated Brain Tumour Boundary Detection using Region based Segmentation Technique along with SVM Classifier

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8364.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2523-2531

Keyword(s):

Brain Tumour ◽

Brain Area ◽

Boundary Detection ◽

Image Features ◽

Surrounding Tissue ◽

Support Vector ◽

Svm Classifier ◽

Efficient Manner ◽

Segmentation Technique ◽

Automated Method

We presents a fully automated method for an automated brain-tumour boundary detection using region based segmentation technique along with SVM Classifier of Magnetic Resonance Imaging (MRI).The procedure is based on artificial intelligence technique and classification of each super-pixel in MRI. A number of novel image features extraction approaches including intensity-based, texture based, fractal analysis and curvatures are calculated from each super-pixel within the entire brain area in MRI to ensure a robust classification. Brain tumor is the malignant types of cancer and their classification in earlier stage is biggest issue. While curable with early classification is useful, only extremely trained specialists are capable of accurately recognizing the cancer from skin MRI data. As expertise is in limited contribute, an automated systems capable of classifying cancer could save human lives, and also help to reduce unnecessary MRI, and reduce extra costs. On the way to achieve this goal, we proposed a Brain Tumour Detection and Classification System (BTDCS) that combines recent developments in machine learning with Support Vector Machine (SVM) structure, creating hybrid algorithm of threshold based segmentation with Maximally Stable External Regions (MSER) that are capable of segmenting accurate super-pixel region from MRI, as well as analyzing the detected area and surrounding tissue for malignant. Using threshold based segmentation technique, the foreground and background component is separated into two regions. To improve the segmentation results, MSER is used with the novel concept of region detection and feature extraction mechanism. The proposed system is evaluated using the largest publicly accessible standard BRATS 2015 dataset of MRI, containing benign and malignant images. When the evaluation parameters of proposed work is compared with a few other state-of-art methods, the proposed means attains the best performance of 98.2% concerning classification accuracy using only the MSER approach and SVM as classifier. The ultimate aim of this research is to devise an automated experimental approach that can segment the tumor boundary in a fast and efficient manner.

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AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

Image Analysis & Stereology ◽

10.5566/ias.1679 ◽

2017 ◽

Vol 36 (2) ◽

pp. 65 ◽

Cited By ~ 7

Author(s):

Elaheh Aghabalaei Khordehchi ◽

Ahmad Ayatollahi ◽

Mohammad Reza Daliri

Keyword(s):

Lung Nodule ◽

Three Dimensional ◽

Pulmonary Nodules ◽

Ct Images ◽

Support Vector ◽

Svm Classifier ◽

Region Merging ◽

Three Dimensional Objects ◽

Ct Data ◽

Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

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PSO-SVM hybrid system for melanoma detection from histo-pathological images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2941-2949 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2941

Author(s):

Maen Takruri ◽

Mohamed Khaled Abu Mahmoud ◽

Adel Al-Jumaily

Keyword(s):

Hybrid System ◽

Wavelet Packet ◽

Automated System ◽

Support Vector ◽

Svm Classifier ◽

Pathology Laboratory ◽

Swarm Optimization ◽

Image Histogram ◽

Melanoma Detection ◽

Occurrence Matrix

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.

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Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

International Journal of Biomedical and Clinical Engineering ◽

10.4018/ijbce.2020070103 ◽

2020 ◽

Vol 9 (2) ◽

pp. 25-44

Author(s):

Usha N. ◽

Sriraam N. ◽

Kavya N. ◽

Bharathi Hiremath ◽

Anupama K Pujar ◽

...

Keyword(s):

Breast Cancer ◽

Classification Accuracy ◽

Breast Imaging ◽

Texture Features ◽

Support Vector ◽

Risk Of Death ◽

Automated Method ◽

Occurrence Matrix ◽

Aided Diagnosis ◽

Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

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