Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

Lotfy, M. M.; El-Bakry, Hazem M.; El-Gayar, M. M.; El–Sappagh, Shaker; I, G. Abdallah M.; Soliman, A. A.; Kwak, Kyung Sup

doi:10.32604/cmc.2022.024193

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…Semantic segmentation is considered one of the computer vision tasks in which each pixel is classified to its semantic label. In contrast to image classification, which classifies the whole image into a single label [12]. The objective of semantic segmentation is to present a comprehensive understanding of the image at the pixel level.…”

Section: A Introductionmentioning

confidence: 99%

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Hasan,

Abdulazeez

2024

ijcs

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As a result of technological advancements, a variety of medical diagnostic systems have grown rapidly to support the healthcare sectors. Over the past years, there has been considerable interest in utilizing deep learning algorithms for the proactive diagnosis of multiple diseases. In most cases, Coronavirus (COVID-19) and tuberculosis (TB) are diagnosed through the examination of pulmonary X-rays. Deep learning algorithms can identify tuberculosis with an almost medical-grade level of consistency by extracting the lung regions in the X-ray images. The probability of tuberculosis detection is increased when classification algorithms are applied to segmented lungs rather than the entire X-ray. The main focus of this paper is to execute lung segmentation from X-ray images using the deeplabv3plus CNN-based semantic segmentation model. In other CNN architectures, the feature resolution diminishes as the network becomes deeper due to the use of sequential convolutions with pooling or striding within the down-sampling stage. To tackle this drawback, deeplabv3plus incorporates "Atrous Convolution" in addition to modifying the pooling and convolutional striding components of the backbone. The experimental results were: an accuracy of 97.42%, a Jaccard index of 93.49%, and a dice coefficient of 96.63%. We also conduct an extensive comparison between the deeplabv3plus segmentation model and other benchmark segmentation architectures. The results prove the ability of the deeplabv3plus model to achieve precise lung segmentation from X-ray images.

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Section: A Introductionmentioning

confidence: 99%

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Hasan,

Abdulazeez

2024

ijcs

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“…This virus has been affected all aspects of our life because it spreads rapidly in all countries and causes acute respiratory distress syndrome [2]. Infected Patients in the first stage are suffering from many common symptoms, including headache, fever, cough, and strong pain in most body muscles [3,4]. Many people died because they suffered from acute pneumonia and shortness of breath [5].…”

Section: Introductionmentioning

confidence: 99%

Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

Alhadad¹,

Tarawneh²,

Mostafa³

et al. 2023

Computers, Materials &Amp; Continua

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is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5-6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach to diagnose COVID-19. It is a novel approach combining residual attention with deep support vector data description (DSVDD) to classify the CT images. To the best of our knowledge, we are the first to combine residual attention with DSVDD in general, and specifically in the diagnosis of COVID-19. Combining attention with DSVDD naively may cause model collapse. Attention in the proposed RADSVDD guides the network during training and enables quick learning, residual connectivity prevents vanishing gradients. Our approach consists of three models, each model is devoted to recognizing one certain disease and classifying other diseases as anomalies. These models learn in an end-to-end fashion. The proposed approach attained high performance in classifying CT images into intact, COVID-19, and non-COVID-19 pneumonia. To evaluate the proposed approach, we created a dataset from published datasets and had it assessed by an experienced radiologist. The proposed approach achieved high performance, with the normal model attained sensitivity (0.96-0.98), specificity (0.97-0.99), F1-score (0.97-0.98), and area under the receiver operator curve (AUC) 0.99; the COVID-19 model attained sensitivity (0.97-0.98), specificity (0.97-0.99), F1-score (0.97-0.99), and AUC 0.99; and the non-COVID pneumonia model attained sensitivity (0.97-1), specificity (0.98-0.99), F1-score (0.97-0.99), and AUC 0.99.

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“…COVID-19 has impacted every aspect of our everyday lives since it spreads fast and causes acute respiratory distress syndrome [2]. Infected people in the initial stage have numerous typical symptoms, including headaches, fevers, coughs, and severe pain in most muscles of the body [3,4]. Many patients perished as a result of severe pneumonia and breathing difculties [5].…”

Section: Introductionmentioning

confidence: 99%

Deep SVDD and Transfer Learning for COVID‐19 Diagnosis Using CT Images

Alhadad

Mostafa

El-Bakry

2023

Computational Intelligence and Neuroscience

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The novel coronavirus disease (COVID-19), which appeared in Wuhan, China, is spreading rapidly worldwide. Health systems in many countries have collapsed as a result of this pandemic, and hundreds of thousands of people have died due to acute respiratory distress syndrome caused by this virus. As a result, diagnosing COVID-19 in the early stages of infection is critical in the fight against the disease because it saves the patient’s life and prevents the disease from spreading. In this study, we proposed a novel approach based on transfer learning and deep support vector data description (DSVDD) to distinguish among COVID-19, non-COVID-19 pneumonia, and intact CT images. Our approach consists of three models, each of which can classify one specific category as normal and the other as anomalous. To our knowledge, this is the first study to use the one-class DSVDD and transfer learning to diagnose lung disease. For the proposed approach, we used two scenarios: one with pretrained VGG16 and one with ResNet50. The proposed models were trained using data gathered with the assistance of an expert radiologist from three internet-accessible sources in end-to-end fusion using three split data ratios. Based on training with 70%, 50%, and 30% of the data, the proposed VGG16 models achieved (0.8281, 0.9170, and 0.9294) for the F1 score, while the proposed ResNet50 models achieved (0.9109, 0.9188, and 0.9333).

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Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

Cited by 3 publications

References 26 publications

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model

Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

Deep SVDD and Transfer Learning for COVID‐19 Diagnosis Using CT Images

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