A SVM Based Model for COVID Detection Using CXR Image

Mohapatra, Sudhir Kumar; Assefa, Beakal Gizachew; Belayneh, Getamesay

doi:10.1007/978-3-030-93709-6_24

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The researcher used machine learning and deep learning for different disease detections, like tuberculosis, breast cancer, and brain tumors [17][18][19][20][21]. They used the chest X-ray (CXR) images as input to the model and finally classified the image as infected or not infected.…”

Section: Literature Reviewmentioning

confidence: 99%

Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques

Mohapatra,

Abebe,

Mekuanint

et al. 2023

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Pneumonia and tuberculosis are the major public health problems worldwide. These diseases affect the lungs, and if they are not diagnosed properly in time, they can become a fatal health problem. Chest x-ray images are widely used to detect and diagnose Pneumonia and Tuberculosis disease. Detection of Pneumonia and Tuberculosis from chest x-ray images is difficult and requires experience due to the similar pathological features of the diseases. Sometimes a misdiagnosis of the disease occurs due to this similarity. Several researchers used deep learning and machine learning techniques to solve this misdiagnosis problem. However, these studies used the chest x-ray images only to develop Pneumonia and Tuberculosis disease detection models. But using the chest x-ray images alone cannot necessarily lead to accurate disease detection and classification. In the traditional or manual approach, medical records are required to support and correctly interpret the chest x-ray images in the appropriate clinical context. This study develops a multi-input Pneumonia and Tuberculosis detection model using chest x-ray images and medical records to follow the clinical procedure. The study applied a Convolutional Neural Network for the chest x-ray image data and a Multilayer perceptron for the medical record data to develop the models. We implemented feature-level concatenation to join the output feature vectors from the Convolutional Neural Network and a Multilayer perceptron for the development of the disease detection model. For the purpose of comparison, we also developed image-only and medical record-only models. Consequently, the image-only model gives an accuracy of 92.68%, the medical record-only model results in 98.72% accuracy, and the combined model accuracy is improved to 99.61%. In general, the study shows that the fusion of the chest x-ray and the medical records leads to better accuracy and is more similar to the clinical approach.

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Section: Literature Reviewmentioning

confidence: 99%

Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques

Mohapatra,

Abebe,

Mekuanint

et al. 2023

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Pandemic Forecasting by Machine Learning in a Decision Support Problem

Sudakov

Titov

2023

Math Models Comput Simul

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Прогнозирование Развития Пандемии Методами Машинного Обучения В Задачах Поддержки Принятия Решений

Sudakov¹,

Titov²

2022

Матем. моделирование

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Предлагается подход, позволяющий на основе достаточно простых моделей предложить методику прогнозирования решения органов управления о количестве необходимых медицинских центров для борьбы с пандемией. В основе него лежит идея о том, что решение об открытии нового центра принимается не сразу при переполнении существующих центров, а с некоторым опозданием. Таким образом, правительство пытается минимизировать риски ненужного открытия и принимает такое решение, понимая, что перегруженность существующих центров не прекратится в ближайшее время. Это решение можно предсказать, обучив модель на исторических данных, полученных из открытых источников. Такая модель разработана нами. Она позволяет прогнозировать количество медицинских центров на основе прогноза количества госпитализированных пациентов за 14 дней. Предложены подходы к прогнозированию количества госпитализированных пациентов с точностью, достаточной для того, чтобы модель предсказывала количество медицинских центров. Модели протестированы на данных из открытых источников, полученных для Рязанской области. Для модели прогноза количества открытых медицинских центров в Рязанской области определены штрафные функции и рассчитаны соответствующие коэффициенты.

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A SVM Based Model for COVID Detection Using CXR Image

Cited by 3 publications

References 31 publications

Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques

Pneumonia and tuberculosis detection with chest x-ray images and medical records using deep learning techniques

Pandemic Forecasting by Machine Learning in a Decision Support Problem

Прогнозирование Развития Пандемии Методами Машинного Обучения В Задачах Поддержки Принятия Решений

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