OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETECTION USING CHEST X-RAY IMAGES

Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CXRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121, 378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision. © 2022 IEEE.

Medical image analysis

This chapter presents deep learning methodologies for medical imaging tasks. The chapter starts with echocardiography for early detection of myocardial infarction (MI) or commonly known as heart attack. Early and fundamental signs of MI can be visible as the abnormality in one or several segments of the left ventricle (LV) wall, where a segment may move “abnormally” or “nonuniformly.” The primary tool to detect and identify such regional wall motion abnormalities is echocardiography, which is a fast, cost-effective, and lowest risk imaging option. A three-phase approach is introduced, where the entire LV wall is segmented by a deep learning model, and then characteristics of the segmented wall are used to perform early detection of MI robustly and accurately. The second medical imaging task discussed in the chapter is the recognition of coronavirus disease 2019 (COVID-19), which has become a global health concern after it is declared as a pandemic in March 2020. Developing automatic, accurate, and fast algorithms for COVID-19 detection plays a vital role in the prevention of spreading the virus. Deep learning models can provide state-of-the-art performance in many imaging tasks. However, due to data scarcity, these models cannot produce satisfactory results when trained for COVID-19 recognition. To tackle this issue, Convolutional Support Estimator Network (CSEN) is introduced due to its advantage over a scarce-data classification task for robust COVID-19 recognition using chest X-ray images. In order to utilize the CSEN classification scheme, features are extracted from a state-of-the-art deep neural network. Consequently, the introduced network can achieve an elegant performance for COVID-19 recognition. © 2022 Elsevier Inc. All rights reserved.

Biosignal time-series analysis

In this chapter, recent state-of-the-art techniques in biosignal time-series analysis will be presented. We shall start with the problem of patient-specific ECG beat classification where the objective is to discriminate the arrhythmic beats from the normal (healthy) beats of an individual patient. So, we will answer the ultimate question of how to design person-specific, real-time, and accurate monitoring of ECG signals. We shall then move on to the recent solution of a related problem, an early warning system that can alert an individual the instant his/her heart deviates from its normal rhythm. This is a far challenging problem since the detection of the arrhythmia beats should be performed without knowing them. © 2022 Elsevier Inc. All rights reserved.

RELIABLE COVID-19 DETECTION USING CHEST X-RAY IMAGES

Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity. © 2021 IEEE