Mucus Plug Blockage Detection in COVID-19 Patient’s Chest X-Ray Using Instance Segmentation

Juyal, Piyush; Sharma, Sachin

doi:10.1007/978-981-16-3660-8_14

Cited by 3 publications

(1 citation statement)

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“…It outperformed seven other DL model based Covid detection and segmentation systems with 91% accuracy and more than 90% sensitivity. Another research on Covid-19 chest X-rays used Mask R-CNN for mucus plug blockage detection in [35]. A CNN model was used for binary classification to identify X-rays of Covid patients.…”

Section: Related Workmentioning

confidence: 99%

Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust

et al. 2022

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Due to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, U-Net, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.

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Section: Related Workmentioning

confidence: 99%