Deep Learning and Probabilistic Neural Networks Based Detection and Classification of Lung Diseases for Pneumonia
Keywords:
Deep Learning, Hybrid Clustering, Classification, Probabilistic Neural Networks Lung Diseases, PneumoniaAbstract
Lung illness is a widespread problem in every region of the globe. Among these are asthma, pneumonia, chronic obstructive pulmonary disease (COPD), fibrosis, and tuberculosis. Detecting lung disease early on is crucial. Several models have been created that use a combination of machine learning and image processing to achieve this goal. Convolutional neural networks (CNN), vanilla neural networks (NN), visual geometry group-based neural networks (VGG), and capsule networks are only some of the well-known deep learning approaches used to predict lung illnesses. Since the publication of the novel Covid-19, numerous research projects focusing on the novel's ability to accurately foresee the future have been started all around the world. Since a number of individuals died from severe chest congestion, the early lung disease known as pneumonia is likely to have a tight connection to Covid-19 (pneumonic condition). The distinction between COVID-19 and other lung disorders, such as pneumonia, can be difficult for medical professionals to make. The most precise method of predicting lung disease is by X-ray imaging of the chest. Using patient chest X-ray images as the data source, we provide a novel framework in this study for the prediction of lung disorders including pneumonia and Covid-19. The system collects data, improves images, analyses regions of interest (ROIs) adaptively and precisely, extracts characteristics, and predicts diseases.
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