FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions In addition, it prevents the spread of this pandemic virus Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed Almost all the methods in the literature are based on individual convolutional neural networks (CNN) Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99% Additionally, the system proved to be reliable as well This is because the sensitivity, specificity, and precision attained to 99% In addition, the diagnostics odds ratio (DOR) is ≥ 100 Furthermore, the results are compared with recent related studies based on the same dataset The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue It can also reduce the time and exertion made by the radiologists during the examination process

[1]  David Dagan Feng,et al.  Classification of Medical Images in the Biomedical Literature by Jointly Using Deep and Handcrafted Visual Features , 2018, IEEE Journal of Biomedical and Health Informatics.

[2]  Wenyu Liu,et al.  Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label , 2020, medRxiv.

[3]  Jie Zhou,et al.  Development and Evaluation of an AI System for COVID-19 , 2020, medRxiv.

[4]  Omneya Attallah,et al.  An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study , 2015, PloS one.

[5]  V. Raudonis,et al.  Embryo development stage prediction algorithm for automated time lapse incubators , 2019, Comput. Methods Programs Biomed..

[6]  Xin Yang,et al.  CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study , 2020, Science China Information Sciences.

[7]  Giacomo Capizzi,et al.  Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning , 2020, IEEE Transactions on Fuzzy Systems.

[8]  Xing Gao,et al.  Integration of deep feature representations and handcrafted features to improve the prediction of N6-methyladenosine sites , 2019, Neurocomputing.

[9]  U. Rajendra Acharya,et al.  Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks , 2020, Computers in Biology and Medicine.

[10]  M. Boukadoum,et al.  Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images , 2013, Journal of medical engineering.

[11]  Ruben Morales-Menendez,et al.  A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images , 2020, Chaos, Solitons & Fractals.

[12]  Vaishali,et al.  Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks , 2020, European Journal of Clinical Microbiology & Infectious Diseases.

[13]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[14]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[15]  Ravindra Kumar Purwar,et al.  A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images , 2017, Appl. Comput. Intell. Soft Comput..

[16]  Marcin Woźniak,et al.  Neural network powered COVID-19 spread forecasting model , 2020, Chaos, Solitons & Fractals.

[17]  Omneya Attallah,et al.  Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection , 2017, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[18]  Omneya Attallah,et al.  Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders , 2020, Diagnostics.

[19]  Bin Zhou,et al.  A regional adaptive variational PDE model for computed tomography image reconstruction , 2019, Pattern Recognit..

[20]  Plamen Angelov,et al.  SARS-CoV-2 CT-scan dataset:A large dataset of real patients CT scans for SARS-CoV-2 identification , 2020 .

[21]  Bradley M. Hemminger,et al.  Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms , 1998, Journal of Digital Imaging.

[22]  Robertas Damasevicius,et al.  A neuro-heuristic approach for recognition of lung diseases from X-ray images , 2019, Expert Syst. Appl..

[23]  Stephen Marshall,et al.  Breast cancer detection using deep convolutional neural networks and support vector machines , 2019, PeerJ.

[24]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[25]  Jun Liu,et al.  Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing , 2020, Radiology.

[26]  Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia , 2020, Radiology of Infectious Diseases.

[27]  Mengjie Zhang,et al.  Differential evolution for filter feature selection based on information theory and feature ranking , 2018, Knowl. Based Syst..

[28]  M. Sharkas,et al.  Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age , 2019, Brain sciences.

[29]  D. Utsunomiya,et al.  Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia , 2020, Japanese Journal of Radiology.

[30]  Raymond Y Huang,et al.  AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT , 2020, Radiology.

[31]  V. Anitha,et al.  Brain tumour classification using two-tier classifier with adaptive segmentation technique , 2016, IET Comput. Vis..

[32]  Maurício Pamplona Segundo,et al.  Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition , 2017, IET Biom..

[33]  Heshui Shi,et al.  Evolution of CT Manifestations in a Patient Recovered from 2019 Novel Coronavirus (2019-nCoV) Pneumonia in Wuhan, China , 2020, Radiology.

[34]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[35]  M. Javaid,et al.  Artificial Intelligence (AI) applications for COVID-19 pandemic , 2020, Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

[36]  A. Amyar,et al.  Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation , 2020, medRxiv.

[37]  K. Cao,et al.  Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy , 2020 .

[38]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[39]  Loris Nanni,et al.  Handcrafted vs. non-handcrafted features for computer vision classification , 2017, Pattern Recognit..

[40]  Kalyani P. Wagh,et al.  Electroencephalograph (EEG) Based Emotion Recognition System: A Review , 2018, Lecture Notes in Networks and Systems.

[41]  David Colquhoun,et al.  An investigation of the false discovery rate and the misinterpretation of p-values , 2014, Royal Society Open Science.

[42]  Yuedong Yang,et al.  Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[43]  Lian-lian Wu,et al.  Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study , 2020, medRxiv.

[44]  Rabha W. Ibrahim,et al.  Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features , 2020, Entropy.

[45]  Xiaolong Qi,et al.  CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia , 2020, Radiology.

[46]  Catharine I Paules,et al.  Coronavirus Infections-More Than Just the Common Cold. , 2020, JAMA.

[47]  Tien Dat Nguyen,et al.  Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors , 2018, Sensors.

[48]  Omneya Attallah,et al.  An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes , 2020, Diagnostics.

[49]  Omneya Attallah,et al.  Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention , 2017, BMC Medical Informatics and Decision Making.

[50]  Charmaine Butt,et al.  Deep learning system to screen coronavirus disease 2019 pneumonia , 2020, Applied Intelligence.

[51]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[52]  F. Shan,et al.  Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia , 2020, Radiology.

[53]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[54]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[55]  Haibo Xu,et al.  AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system , 2020, Applied Soft Computing.

[56]  Omneya Attallah,et al.  Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers , 2019, Diagnostics.

[57]  X. He,et al.  Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans , 2020, medRxiv.

[58]  Z. Fayad,et al.  CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) , 2020, Radiology.

[59]  Palaiahnakote Shivakumara,et al.  A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement , 2018, Entropy.

[60]  E. Holmes,et al.  A new coronavirus associated with human respiratory disease in China , 2020, Nature.