Abstract
Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head. DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries. The model was trained using fine-tuned hyperparameters selected after extensive experimentation. Subsequently, the Gray Level Co-occurrence Matrix (GLCM) features and statistical features including circularity, area, and perimeters were computed for each segmented image. The computed features were serially fused and the best features (those that were optimally discriminatory) selected using a Genetic Algorithm (GA) for classification. The performance of the method was evaluated using two benchmark datasets: The COVID-19 Segmentation and the POF Hospital datasets. The results were better than those of existing methods.
Highlights
COVID-19 infection can be diagnosed with high sensitivity using a chest X-ray; the disease correlates with certain visual indices [7,8]
The principal steps of our approach are: Pre-processing is performed using a Gabor filter to enhance image intensities, and a marker controller watershed with thresholding is used to segment the actual lung region
Artificial intelligence methods play important roles when learning the patterns of many Computed tomography (CT) images and making predictions based on those patterns [23,24,25,26]
Summary
SARS-CoV-2 ( known as COVID-19) is a novel coronavirus. The disease caused by the virus has been declared a pandemic by the World Health Organization (WHO) and has spread to more than 170 countries worldwide. The WHO has stated that 33,842,281 COVID-19 cases have been confirmed worldwide, as have 1,010,634 deaths. In Pakistan, COVID-19 is spreading rapidly; a recent report described 312,263 confirmed cases, 6,479 deaths, and 296,881 recoveries. The treatment of such patients in special care units requires early detection of COVID-19 to increase the survival rate. Separation of healthy people from affected patients is the prime objective; this is possible only if diagnosis is early. Given the shortage of trained radiologists and the huge numbers of affected patients, automatic abnormality identification would assist early diagnosis. The deeplabv network serves as the bottleneck of the moblenetv module The combination of these convolutional neural networks accurately segments the infected lung region. The structure of the manuscript is as follows: in Section 2, related work is described; in Section 3, our work is explained; in Section 4, the findings and discussion appear; and, in Section 5, conclusions are drawn
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