"Innovative Insights: Unleashing Machine Learning for Precise COVID-19 CT Scan Diagnosis"
Keywords:
COVID-19, disease detection, CT scan images, machine learning, deep learning, CNN, performance analysisAbstract
Effectively managing and mitigating the impact of COVID-19 requires swift and accurate identification of cases. This study explored the use of CT scan images to diagnose COVID-19 infections and evaluated the effectiveness of various approaches based on deep learning and machine learning methodologies. LSTM, ResNet50, MobileNet, KNN, SVM, decision tree, Nave Bayes, logistic regression, CNN, and LSTM were used for training and evaluation. Measures, including precision, accuracy, recall, F score, and false prediction rate, are computed using CT scan image collection and preprocessing. Our results show the remarkable performance of the CNN algorithm, which achieved 100% accuracy, 100% recall, 100% F score, 100% precision, and a 0% false prediction rate. Based on the comparison analysis, both the KNN and SVM algorithms showed promising results. These results suggest that COVID-19 patients can be reliably identified from CT images using deep learning and machine learning techniques. Subsequent investigations should explore transfer learning methodologies and ensemble models and amalgamate many modalities to enhance the algorithms' overall applicability and precision in diagnosis.
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