Hashemipoor, Sayed Mansour

This research presented an AI-driven methodology for precise COVID-19 detectionin medical diagnostic imaging using chest X-ray images. The primary focus was on developing an optimized model using convolutional neural networks (CNNs) and leveraging transfer learning as a low-level feature extractor method. Significant attention was also so given to data transformation and enhancement techniques to improve the information content and distinguishability of non-linearities. The proposed methodology enabled the flexibility to apply multiple models within the framework and identify the most suitable model for the specific task at hand. By emphasizing state-of-the-art CNN models and employing a strategic exploration-exploitation trade-off, this study identified a robust model with heightened accuracy. The results demonstrated a model accuracy of 90.11%, a sensitivity of 91.16%, and a precision of 89.19%, highlighting the model's effectiveness in accurately identifying both true positive and true negative test cases.

Author Keywords: Automatic Bayesian Optimization, Convolution Neural Networks, COVID- 19 Detection, Deep Learning, Medical Diagnostic Imaging, Transfer Learning