Classification and Region Analysis of COVID-19 Infection Using Lung CT Images and Deep Convolutional Neural Networks

Khan, Saddam Hussain; Sohail, Anabia; Khan, Asifullah; Lee, Yeon Soo

doi:10.2139/ssrn.4029649

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…In medical image analysis, many research horizons are explored. These include various areas of medical imaging, such as detection, classification, and segmentation [ 4 , 5 , 6 , 7 , 8 , 9 ]. As cohorts build for brain tumor classification, there is a gap for novel approaches related to feature extraction by using limited and class-unbalanced MR images datasets of brain tumors and tumors from other parts of the human body [ 10 , 11 ].…”

Section: Related Workmentioning

confidence: 99%

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Zahoor

Qureshi

Bibi

et al. 2022

Sensors

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Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

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Section: Related Workmentioning

confidence: 99%

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Zahoor

Qureshi

Bibi

et al. 2022

Sensors

Self Cite

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“…Khan et al 4 . introduced a new CNN-based technique to analyze the COVID-19 anomalies in chest CT samples with classification and segmentation stages.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To detect the disease by RT-PCR, normally 2 days is required, and sometimes it suffers from some inherited limitation. Therefore, X-rays and computed tomography devices could serve as a reliable, valuable, and rapid technique in the detection and evaluation of COVID-19 4 …”

Section: Introductionmentioning

confidence: 99%

(Retracted) Review of artificial intelligence-assisted COVID-19 detection solutions using radiological images

Yousefpanah

Ebadi

2022

J. Electron. Imag.

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A new virus called coronavirus has been affecting the world since 2019 and has killed millions of people. Even though vaccines for the virus have been developed and the mortality rate is decreasing across the world, many countries are still struggling with the pandemic. Artificial intelligence (AI) methods have been regarded as fast techniques and powerful tools for screening this disease. We reviewed papers that used AI-based systems for the diagnosis of COVID-19 using radiological images, such as X-rays and computed tomography (CT) images. This survey focuses specifically on deep learning (DL)-based systems for screening COVID-19 patients. Privacy and accuracy of diagnosis are of paramount importance in a clinical environment. In most surveys, the privacy issue is not taken into consideration. In this regard, we categorize recent work into three taxonomies: federated learning (FL) models (privacy-guarding methods), ensemble machine learning (ML) models, and other ML and DL models. A summary of the selected articles is presented; parameters such as the modality, experimental tools, data sources, number of classes, and positive and nonpositive aspects of each model and work, as well as evaluation measures, are depicted. In fact, we compare papers and their experimental results to find more accurate and privacy-guarding methods. Also, the type of data and tools that are helpful for more accurate prediction were investigated. Finally, we refer to some limitations of ML methods and provide useful insight for future researchers. In this survey, 45% and 41% of papers used X-ray and CT images for experiments, respectively. Using multiple datasets was the preference of 61% of researchers, and 45% of papers considered binary classification. The average accuracy of 95.71%, 97.09%, and 93.38% was obtained for federated ML, ensemble ML, and other ML models, respectively. To sum up, X-ray images were the favorite of most articles. Also, most researchers employed multiple databases for their experiments, and binary classification was the method of choice for most of them. Among the three categories, ensemble learningbased systems demonstrated the best performance in terms of all evaluation metrics. Therefore, these systems can be used to screen COVID-19 patients.

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Autonomous Drone Swarm Navigation in Complex Environments

Qamar

Shahbaz

et al. 2022

2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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Classification and Region Analysis of COVID-19 Infection Using Lung CT Images and Deep Convolutional Neural Networks

Cited by 3 publications

References 43 publications

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

(Retracted) Review of artificial intelligence-assisted COVID-19 detection solutions using radiological images

Autonomous Drone Swarm Navigation in Complex Environments

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