COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

Hasoon, Jamal N.; Fadel, Ali; Hameed, Rasha Subhi; Mostafa, Salama A.; Khalaf, Bashar Ahmed; Mohammed, Mazin Abed; Nedoma, Jan

doi:10.1016/j.rinp.2021.105045

Cited by 59 publications

(25 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI, such as machine and deep neural network (DNN) techniques [ 5 ], has been increasingly used in recent years as the main tool to find solutions to diverse difficulties, such as object detection [ 6 – 8 ], image classification [ 9 ], and speech recognition [ 10 ]. In image processing [ 11 ], a convolutional neural network (CNN) has specifically produced outstanding results [ 12 ]. Many studies have presented the influence and strength of these techniques in image segmentation [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Abdulkareem

Mostafa

Al-Qudsy

et al. 2022

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…There were 125,000 cases reported to WHO from 115 countries and territories, while the number of cases reported outside China also nearly doubled within a couple of weeks, and the number of nations affected nearly tripled [ 2 ]. Statistically, 305,914,601 people worldwide tested positive for COVID-19 and 5,486,304 people died as a result up to 10 January 2022 [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

Shamim

Awan

Zain

et al. 2022

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-19 up to 10 January 2022. CT scans can be used as an alternative to time-consuming RT-PCR testing for COVID-19. This research work proposes a segmentation approach to identifying ground glass opacity or ROI in CT images developed by coronavirus, with a modified structure of the Unet model having been used to classify the region of interest at the pixel level. The problem with segmentation is that the GGO often appears indistinguishable from a healthy lung in the initial stages of COVID-19, and so, to cope with this, the increased set of weights in contracting and expanding the Unet path and an improved convolutional module is added in order to establish the connection between the encoder and decoder pipeline. This has a major capacity to segment the GGO in the case of COVID-19, with the proposed model being referred to as “convUnet.” The experiment was performed on the Medseg1 dataset, and the addition of a set of weights at each layer of the model and modification in the connected module in Unet led to an improvement in overall segmentation results. The quantitative results obtained using accuracy, recall, precision, dice-coefficient, F1score, and IOU were 93.29%, 93.01%, 93.67%, 92.46%, 93.34%, 86.96%, respectively, which is better than that obtained using Unet and other state-of-the-art models. Therefore, this segmentation approach proved to be more accurate, fast, and reliable in helping doctors to diagnose COVID-19 quickly and efficiently.

show abstract

“…The ReLU function was used because it avoids and corrects the vanishing gradient problem. Neural network models that employ ReLU are easier to train and perform better than models that employ other activation functions such as sigmoid or hyperbolic tangent activation functions [20] .…”

Section: Methodsmentioning

confidence: 99%

Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients

Ayalew

Salau

Abeje

et al. 2022

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

COVID-19 is now regarded as the most lethal disease caused by the novel coronavirus diseases for humans. The COVID-19 pandemic has spread to every country on the planet and has wreaked havoc on them by increasing the number of human deaths, caused intense hunger, and lowered economic productivity. Also due to a lack of sufficient radiologist, restricted amount of COVID-19 test kits available in hospitals, and a shortage of equipment due to the daily increase in cases, the number of persons infected with COVID-19 has increased. Even for experienced radiologists, examining chest X-rays is a difficult task. Many people have died as a result of inaccurate COVID-19 diagnosis and treatment, as well as ineffective detection measures. This paper therefore presents a unique detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients. To achieve quick diagnosis, a convolutional neural network (CNN) and histogram of oriented gradients (HOG) method is proposed in this paper to help medical experts diagnose COVID-19 disease. The diagnostic performance of the hybrid CNN model and HOG-based method was then evaluated using chest X-ray images collected from University of Gondar and online databases. This was achieved using Keras (with TensorFlow as a backend) and Python. After the DCCNet model was evaluated, a 99.9% training accuracy and 98.3% test accuracy was achieved, while a 100% training accuracy and 98.5% test accuracy was achieved using HOG. After evaluation, the hybrid model achieved 99.97% and 99.67% training and testing accuracy for detection and classification of COVID-19 which was better by 1.37% than when feature extraction was performed using CNN and 1.17% when HOG was used. The DCCNet produced a result that outperformed state-of-the-art models by 6.7%.

show abstract

COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images

Cited by 59 publications

References 25 publications

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model

Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients

Contact Info

Product

Resources

About