scholarly journals Segmentation and Detection of Brain Tumor by using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 3226-3235
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Detection System ◽  
Research Area ◽  
Support Vector ◽  
Subsequent Stage ◽  
Gray Level ◽  
K Nearest Neighbors ◽  
Classification Techniques ◽  
Occurrence Matrix

The segmentation and detection of brain pathologies in medical images is an indispensible step. This helps the radiologist to diagnose a variety of brain deformity and helps in the set up for a suitable treatment. Magnetic Resonance Imaging (MRI) plays a significant character in the research area of neuroscience. The proposed work is a study and probing of different classification techniques used for automated detection and segmentation of brain tumor from MRI in the field of machine learning. This paper try to present the feature extraction from raw MRI and fed the same to four classifier named as, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). This mechanism was done in various stages for Computer Aided Detection System. In the preliminary stage the pre-processing and post-processing of MR image enhancement is done. This was done as the processed image is more likely suitable for the analysis. Then the k-means clustering is used to sectioning the MRI by applied mean gray level method. In the subsequent stage, statistical feature analysis were done, the features were computed using Haralick’s equation for feature based on the Gray Level Co-occurrence Matrix. Feature chosen dependent on tumor region, location, periphery, and color from the sectioned image is then classified by applying the classification techniques. In the third stage SVM, DT, ANN, and KNN classifiers were used for diagnoses. The performances of the classifiers are tested and evaluated successfully.

scholarly journals An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images

2019 ◽  
Author(s):  
Pedro Pedrosa Rebouças Filho ◽  
Suane Pires Pinheiro Da Silva ◽  
Jefferson Silva Almeida ◽  
Elene Firmeza Ohata ◽  
Shara Shami Araujo Alves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Chronic Kidney Diseases ◽  
Occurrence Matrix

Chronic kidney diseases cause over a million deaths worldwide every year. One of the techniques used to diagnose the diseases is renal scintigraphy. However, the way that is processed can vary depending on hospitals and doctors, compromising the reproducibility of the method. In this context, we propose an approach to process the exam using computer vision and machine learning to classify the stage of chronic kidney disease. An analysis of different features extraction methods, such as Gray-Level Co-occurrence Matrix, Structural Co-occurrence Matrix, Local Binary Patters (LBP), Hu's Moments and Zernike's Moments in combination with machine learning methods, such as Bayes, Multi-layer Perceptron, k-Nearest Neighbors, Random Forest and Support Vector Machines (SVM), was performed. The best result was obtained by combining LBP feature extractor with SVM classifier. This combination achieved accuracy of 92.00% and F1-score of 91.00%, indicating that the proposed method is adequate to classify chronic kidney disease in two stages, being a high risk of developing end-stage renal failure and other outcomes, and otherwise.

scholarly journals Perbandingan Metode Klasifikasi pada Pengolahan Citra Mata Ikan Tuna

2020 ◽  
Vol 5 ◽  
Author(s):  
Toni Dwi Novianto ◽  
I Made Susi Erawan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Region Of Interest ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Gray Level ◽  
K Nearest Neighbors ◽  
Eye Color ◽  
Occurrence Matrix ◽  
Fish Eye

<p class="AbstractEnglish"><strong>Abstract:</strong> Fish eye color is an important attribute of fish quality. The change in eye color during the storage process correlates with freshness and has a direct effect on consumer perception. The process of changing the color of the fish eye can be analyzed using image processing. The purpose of this study was to obtain the best classification method for predicting fish freshness based on image processing in fish eyes. Three tuna fish were used in this study. The test was carried out for 20 hours with an eye image every 2 hours at room temperature. Fish eye image processing uses Matlab R.2017a software while the classification uses Weka 3.8 software. The image processing stages are taking fish eye image, segmenting ROI (region of interest), converting RGB image to grayscale, and feature extraction. Feature extraction used is the gray-level co-occurrence matrix (GLCM). The classification techniques used are artificial neural networks (ANN), k-neighborhood neighbors (k-NN), and support vector machines (SVM). The results showed the value using ANN = 0.53, k-NN = 0.83, and SVM = 0.69. Based on these results it can be determined that the best classification technique is to use the k-nearest neighbor (k-NN).</p><p class="AbstrakIndonesia"><strong>Abstrak:</strong> Warna mata ikan merupakan atribut penting pada kualitas ikan. Perubahan warna mata ikan selama proses penyimpanan berhubungan dengan tingkat kesegaran dan memiliki efek langsung pada persepsi konsumen. Proses perubahan warna mata ikan dapat dianalisis menggunakan pengolahan citra. Tujuan penelitian ini adalah mendapatkan metode klasifikasi terbaik untuk memprediksi kesegaran ikan berbasis pengolahan citra pada mata ikan. Tiga ekor ikan tuna digunakan dalam penelitian ini. Pengujian dilakukan selama 20 jam dengan pengambilan citra mata setiap 2 jam pada suhu ruang. Pengolahan citra mata ikan menggunakan software matlab R.2017a sedangkan pengklasifiannya menggunakan software Weka 3.8. Tahapan pengolahan citra meliputi pengambilan citra mata ikan, segmentasi ROI (<em>region of interest</em>), konversi citra RGB menjadi <em>grayscale</em>, dan ekstraksi fitur. Ekstraksi fitur yang digunakan yaitu <em>gray-level co-occurrence matrix</em> (GLCM).  Teknik klasifikasi yang digunakan yaitu, <em>artificial neural network</em> (ANN), <em>k-nearest neighbors</em> (k-NN), dan <em>support vector machine</em> (SVM). Hasil penelitian menunjukkan nilai korelasi menggunakan ANN = 0,53, k-NN = 0,83, dan SVM = 0,69. Berdasarkan hasil tersebut dapat disimpulkan teknik klasifikasi terbaik adalah menggunakan <em>k-nearest neighbors</em> (k-NN).</p>

scholarly journals An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

2019 ◽  
Vol 9 (18) ◽  
pp. 3723
Author(s):  
Sharif ◽  
Mumtaz ◽  
Shafiq ◽  
Riaz ◽  
Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Principal Component ◽  
Research Area ◽  
Ensemble Classification ◽  
High Dimensional ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
N Gram ◽  
Low Dimensional

The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency—Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naïve Bayes’, K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from n-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods.

Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix

2020 ◽  
pp. 239-255
Author(s):  
Khaddouj Taifi ◽  
Naima Taifi ◽  
Mohamed Fakir ◽  
Said Safi ◽  
Muhammad Sarfraz
Keyword(s):  
Region Of Interest ◽  
Contourlet Transform ◽  
Support Vector ◽  
Gray Level ◽  
Nonsubsampled Contourlet Transform ◽  
Mass Detection ◽  
K Nearest Neighbors ◽  
Cad System ◽  
Occurrence Matrix ◽  
Breast Tissues

This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT.

scholarly journals Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

2020 ◽  
Author(s):  
Chenglong Liu ◽  
Xiaoyang Wang ◽  
Chenbin Liu ◽  
Qingfeng Sun ◽  
Wenxian Peng
Keyword(s):  
Machine Learning ◽  
Characteristic Curve ◽  
Medical Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Support Vector ◽  
Equal Weight ◽  
Gray Level ◽  
Novel Coronavirus ◽  
Occurrence Matrix

Abstract Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

scholarly journals Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

2020 ◽  
Author(s):  
Chenglong Liu ◽  
Xiaoyang Wang ◽  
Chenbin Liu ◽  
Qingfeng Sun ◽  
Wenxian Peng
Keyword(s):  
Machine Learning ◽  
Characteristic Curve ◽  
Medical Image Analysis ◽  
Ct Images ◽  
Chest Ct ◽  
Support Vector ◽  
Equal Weight ◽  
Gray Level ◽  
Novel Coronavirus ◽  
Occurrence Matrix

Abstract Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented base on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

scholarly journals Iris-based Image Processing for Cholesterol Level Detection using Gray Level Co-Occurrence Matrix and Support Vector Machine

2020 ◽  
Vol 24 (5) ◽  
pp. 135-144
Author(s):  
Melvin Daniel ◽  
Jangkung Raharjo ◽  
Koredianto Usman
Keyword(s):  
Support Vector Machine ◽  
Correlation Energy ◽  
Detection System ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Gray Level ◽  
Quantization Level ◽  
Iris Image ◽  
Cholesterol Levels ◽  
Occurrence Matrix

Serious illnesses such as strokes and heart attacks can be triggered by high levels of cholesterol in human blood that exceeds ideal conditions, where the ideal cholesterol level is below 200 mg/dL. To find out cholesterol levels need a long process because the patient must go through a blood sugar test that requires the patient to undergo fasting for 10–12 hours first before the test. Iridology is a branch of science that studies human iris and its relation to the wellness of human internal organs. The method can be used as an alternative for medical analysis. Iridology thus can be used to assess the conditions of organs, body construction, and other psychological conditions. This paper proposes a cholesterol detection system based on the iris image processing using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM). GLCM is used as the feature extraction method of the image, while SVM acts as the classifier of the features. In addition to GLCM and SVM, this paper also construct a preprocessing method which consist of image resizing, segmentation, and color image to gray level conversion of the iris image. These steps are necessary before the GLCM feature extraction step can be applied. In principle, the GLCM method is a construction of a matrix containing the information about the proximity position of gray level images pixels. The output of GLCM is fed to the SVM that relies on the best hyperplane. Thus, SVM performs as a separator of two data classes of the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes, namely: non–cholesterol (< 200 ), risk of cholesterol (200–239 ) and high cholesterol (> 240 ). The accuracy rate obtained was 94.67% with an average computation time of 0.0696 . It was using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass. Iris has specific advantages which can record all organ conditions, body construction and psychological conditions. Therefore, Iridology as a science based on the arrangement of iris fibers can be an alternative for medical analysis. In this paper proposes a cholesterol detection system through the iris using Gray Level Co-occurence Matrix and Support Vector Machine. Input system is an iris image that will be processed by pre-processing and then extracted features with the Gray Level Co-Occurrence Matrix method which is a matrix containing information about position the proximity of pixels that have a certain gray level. And then classified with the Support Vector Machine method that relies on the best hyper lane which functions as a separator of two data classes in the input space. From the simulation results, the system built was able to detect excess cholesterol levels through iris image and classify into three classes are: risk of cholesterol, high cholesterol and non–cholesterol with an accuracy rate of 96.47% and average computation time was 0.0696 using each of the 75 training and test data, with the second-order parameters used are contrast–correlation–energy–homogeneity, pixel distance = 1, quantization level = 8, Polynomial kernel types and One Against One Multiclass.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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