The coronavirus disease of 2019 (COVID-19), a unique Coronavirus strain, has created a chaotic situation, negatively impacting the number of deaths and people's lives globally. The daily increase in COVID-19 instances is due to a lack of and restricted availability of detection techniques for determining the disease's presence. Therefore, detecting positive results as soon as feasible is important to preventing the spread of this epidemic and treating infected people as soon as possible. As a result of these constraints, the demand for clinical decision-making systems based on predictive algorithms has increased. The article describes a recurrent neural network (RNN) for identifying Coronavirus (COVID-19) and tries to improve the detection method. Different machine learning methodologies, such as Support Vector Machines (SVMs), were used to create a detection system with a deep learning algorithm called Long Short Term Memory (LSTM). The research describes a method for detecting COVID-19 in tagged CT images of patients. Various common picture features, such as central moments, Gabor wavelets, and GLCM-related features, are discussed. Ant colony optimization-ant lion optimization (ACO-ALO) is used to select optimum subsets of SVM parameters. The results show that SVM parameters such as penalty and kernel parameters have a positive effect on SVM model correctness and complexity. Besides, the findings revealed that the proposed method may be employed as a system of aid to diagnose COVID-19 disease. The findings uncover that the suggested strategy has promising behavior in terms of increasing classification accuracies as well as optimal feature selection. Promisingly, the presented strategy can be regarded as a useful clinical decision-making tool for clinicians.
With today's rapid increase in population, automatic diagnosis of disease has emerged as a critical subject in the field of medicine. An automatic disease detection framework gives correct, accurate, and rapid outputs, supporting clinicians in making accurate diagnoses while also reducing the number of deaths from disease. This paper aims to develop a system for detecting anomalies, and to reach this objective, various concepts of machine learning, for example, support vector machines, are employed for the construction of a detection system with a deep learning algorithm, termed long short-term memory. Accordingly, the study proposes an approach for detecting lung involvement. A dataset consisting of 4575 CT scan pictures, of which 1525 were from the COVID-19 virus, was employed in the current investigation. The proposed system attained 99.9487% accuracy, 99.9485% specificity, 99.9485% sensitivity, and a 99.8787% F1-score based on experimental results. Based on the data provided, the system was able to accomplish the expected results. The experimental findings highlight the favorable features in improving classification accuracy and selecting the best attributes. The method thus shows promise as a convenient diagnostic tool to aid physicians in clinical decision-making.
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