Support Vector Machine Approach for the Diagnosis of Arthritis From Digital X-Ray Images Using Local Ternary Pattern

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel (CSVMPol) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.

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Caries Detection in Panoramic Dental X-ray Images Using Support Vector Machine

International Journal of Science and Applied Information Technology ◽

10.30534/ijsait/2019/04862019 ◽

2019 ◽

Vol 8 (6) ◽

pp. 40-44

Author(s):

Muna Khalid Jasim

Keyword(s):

Support Vector Machine ◽

Caries Detection ◽

Support Vector ◽

X Ray

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Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19

Teknika ◽

10.34148/teknika.v10i2.331 ◽

2021 ◽

Vol 10 (2) ◽

pp. 96-103

Author(s):

Mohammad Farid Naufal ◽

Selvia Ferdiana Kusuma ◽

Kevin Christian Tanus ◽

Raynaldy Valentino Sukiwun ◽

Joseph Kristiano ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Cross Validation ◽

Nearest Neighbor ◽

Nearest Neighbors ◽

Support Vector ◽

K Nearest Neighbor ◽

K Nearest Neighbors ◽

X Ray ◽

Chest X Ray

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

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Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽

10.1371/journal.pone.0242899 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242899

Author(s):

Musatafa Abbas Abbood Albadr ◽

Sabrina Tiun ◽

Masri Ayob ◽

Fahad Taha AL-Dhief ◽

Khairuddin Omar ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Support Vector Machine ◽

Ct Scan ◽

Extreme Learning Machine ◽

Experimental Result ◽

Support Vector ◽

X Ray ◽

Chest X Ray ◽

Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

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Object detection in X-ray images based on object candidate extraction and support vector machine

2013 Ninth International Conference on Natural Computation (ICNC) ◽

10.1109/icnc.2013.6817965 ◽

2013 ◽

Cited By ~ 1

Author(s):

Yan Wang ◽

Jing Huang

Keyword(s):

Support Vector Machine ◽

Object Detection ◽

Support Vector ◽

X Ray

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CLASSIFICATION OF PNEUMONIA USING SUPPORT VECTOR MACHINE

Jurnal Algoritme ◽

10.35957/algoritme.v1i1.429 ◽

2020 ◽

Vol 1 (1) ◽

pp. 21-32

Author(s):

Risha Ambar Wati ◽

Hafiz Irsyad ◽

Muhammad Ezar Al Rivan

Keyword(s):

Support Vector Machine ◽

Lung Disease ◽

Extraction Method ◽

Classification Method ◽

Support Vector ◽

Gray Level ◽

X Rays ◽

X Ray ◽

Contrast Stretching

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by x-ray. X-rays will be analyzed to determine whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or not on the x-ray results. The classification method used in this study were Support Vector Machine (SVM) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before classification, namely cropping, resizing, contrast stretching, and thresholding then extracted using GLCM and classified using SVM. The results showed that the best accuracy of 62.66%.

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A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

Open Access Macedonian Journal of Medical Sciences ◽

10.3889/oamjms.2021.6955 ◽

2021 ◽

Vol 9 (B) ◽

pp. 1283-1289

Author(s):

Jane Aurelia ◽

Zuherman Rustam

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computed Tomography ◽

Support Vector Machine ◽

Support Vector ◽

X Ray ◽

Network Support ◽

Computed Tomography Images ◽

X Ray Computed

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

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