A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Saygılı, Ahmet

doi:10.1016/j.asoc.2021.107323

Cited by 108 publications

(59 citation statements)

References 58 publications

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“… 54 Proposed a genome analysis of COVID‐19 by using the AI. 58 Both chest x‐ray images and CT scan based automatic detection of corona virus is presented in References 59 , 60 , 61 . For analysis and detection of coronavirus a DL model is presented by Kedia et al 62 In the field AI and ML, TL is the most important and extensively used methodology that works based on previously trained ML models and re‐use the relevant knowledge of these trained models to different tasks.…”

Section: Related Workmentioning

confidence: 99%

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

Umer

Amin

Sharif

et al. 2021

Concurrency and Computation

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Summary COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

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Section: Related Workmentioning

confidence: 99%

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

Umer

Amin

Sharif

et al. 2021

Concurrency and Computation

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“…Saygili [38] used three different data sets of thoracic CT and CXR from different countries and applied classical steps, i.e., preprocessing, feature extraction, and classification. The feature extractors included histogram of oriented gradient, gray-level co-occurrence matrices, scale-invariant feature transform, and local binary pattern (LBP).…”

Section: Handcrafted Radiomicsmentioning

confidence: 99%

Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications

Mohammadi

Wang

Enshaei

et al. 2021

IEEE Signal Process. Mag.

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T he novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients.We intend to present not only an overview of the current state, challenges, and opportunities of developing SP/ DL-empowered models for the diagnosis/prognosis of COVID-19 but also the latest developments in the theoretical framework of AE and hypersignal processing (HP) for COVID-19 from the points of view of both SP and medical/pandemic control professionals. The imaging modalities and radiological characteristics of COVID-19 are then discussed. SL/DL-based radiomic models specific to the analysis of COVID-19 infection are described covering four domains, which encompass the segmentation of COVID-19 lesions, models for outcome prediction, severity assessment, and diagnosis/classification models. This work leads to the identification of significant open problems and opportunities

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“…With the recent outbreak of the deadly corona virus (Covid-19), the timely detection of pulmonary diseases become even more important and the entire world is looking for such a solution. In this regard, the authors in [36]- [38] have used the chest x-ray, CT scan images and both respectively to diagnose the presence of the virus in a timely fashion. The authors in [36] used five datasets naming COVID-DB, COVID-19, COVID-19-AR, NIH chest x-ray and Pneumonia Chest x-ray datasets.…”

Section: A Classification Based On Entire Input Imagementioning

confidence: 99%

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

et al. 2021

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Interpreting chest x-ray (CXR) to find anomalies in the thoracic region is a tedious job and can consume an ample amount of radiologist's time when there are thousands of them to process. In such scenarios, the Computer-Aided Diagnostic (CAD) systems can help radiologists by doing the trivial processing and presenting the information in a meaningful way so that, the radiologist can make more accurate decisions by spending less amount of time and energy. This research study intends to propose a better, accurate, and efficient CNN based pulmonary disease diagnosis system using CXR images. In the proposed system, the capabilities of deep neural network architecture are exploited by proposing a custom CNN architecture with additional layers and modified hyperparameters to meet the required results. The input CXR is examined for healthy or infected at the surface level and the infected images are further processed for class level label classification. The lung region is segmented from the entire input CXR image to reduce the amount of noise and increase the processing efficiency by processing less overall information. The proposed model is evaluated on the benchmark split of the NIH chest x-ray dataset and achieves better segmentation and classification results when compared to the state of the art approaches.

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A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods

Cited by 108 publications

References 58 publications

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph

Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications

Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

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