Pneumonia is a severe respiratory infection that can cause life-threatening complications if left untreated. Although an early and accurate diagnosis is essential for successful treatment, conventional techniques of diagnosis can be expensive and time-consuming. For the automated detection of pneumonia from medical images, deep learning algorithms and computer vision methods have recently been investigated. In this study, we suggest a system for automatically identifying pneumonia from chest X-ray images using deep learning algorithms. In order to categorize chest X-ray pictures as either pneumonia-positive or pneumonia-negative, we will create a convolutional neural network (CNN) model that will be trained on a dataset of chest X-ray images. Using different assessment metrics, such as accuracy, sensitivity, and specificity, we will assess the model's performance. Particularly in re-source-constrained settings with a shortage of skilled medical personnel, the suggested system has the potential to greatly improve the effectiveness and accuracy of pneumonia diagnosis. The system is a useful instrument for the medical community because of its capacity to provide early and accurate diagnosis, which may be able to save lives and enhance patient outcomes to a considerable degree.
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Detection of Pneumonia using Machine Learning
Semantic Scholar · Medicine · 2023
Abstract
Pneumonia is a severe respiratory infection that can cause life-threatening complications if left untreated. Although an early and accurate diagnosis is essential for successful treatment, conventional techniques of diagnosis can be expensive and time-consuming. For the automated detection of pneumonia from medical images, deep learning algorithms and computer vision methods have recently been investigated. In this study, we suggest a system for automatically identifying pneumonia from chest X-ray images using deep learning algorithms. In order to categorize chest X-ray pictures as either pneumonia-positive or pneumonia-negative, we will create a convolutional neural network (CNN) model that will be trained on a dataset of chest X-ray images. Using different assessment metrics, such as accuracy, sensitivity, and specificity, we will assess the model's performance. Particularly in re-source-constrained settings with a shortage of skilled medical personnel, the suggested system has the potential to greatly improve the effectiveness and accuracy of pneumonia diagnosis. The system is a useful instrument for the medical community because of its capacity to provide early and accurate diagnosis, which may be able to save lives and enhance patient outcomes to a considerable degree.