Abstract
COVID-19 pandemic is a deadly disease spreading very fast. People with the confronted immune system are susceptible to many health conditions. A highly significant condition is pneumonia, which is found to be the cause of death in the majority of patients. The main purpose of this study is to find the volume of GGO and consolidation of a COVID-19 patient, so that the physicians can prioritize the patients. Here, we used transfer learning techniques for segmentation of lung CTs with the latest libraries and techniques which reduces training time and increases the accuracy of the AI Model. This system is trained with DeepLabV3 + network architecture and model ResNet50 with ImageNet weights. We used different augmentation techniques like Gaussian noise, horizontal shift, color variation, etc., to get to the result. Intersection over Union (IoU) is used as the performance metrics. The IoU of lung masks is predicted as 99.78% and that of infected masks is as 89.01%. Our work effectively measures the volume of infected region by calculating the volume of infected and lung mask region of the patients.
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Sabeerali, K.P., Saleena, T.S., Muhamed Ilyas, P., Neha Mohan (2022). AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT Images in COVID-19 Patients. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Emergent Converging Technologies and Biomedical Systems . Lecture Notes in Electrical Engineering, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-16-8774-7_9
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DOI: https://doi.org/10.1007/978-981-16-8774-7_9
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