A CNN-Based Chest Infection Diagnostic Model: A Multistage Multiclass Isolated and Developed Transfer Learning Framework

In 2019, a deadly coronaviral infection (COVID-19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID-19 may be found using a chest X-ray scan, a reliable imaging method. Although an expert may examine an X-ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19-layer CNN model is developed from scratch to detect chest infections using X-rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID-19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four-class subclassification (COVID-19, fibrosis, pneumonia, and tuberculosis) for an online chest X-ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.

[1]  Xiangxi Wang,et al.  Enhanced transmissibility of XBB.1.5 is contributed by both strong ACE2 binding and antibody evasion , 2023, bioRxiv.

[2]  M. Alhaisoni,et al.  D2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI Scans , 2022, Diagnostics.

[3]  N. Vaegae,et al.  Development of CNN-LSTM combinational architecture for COVID-19 detection , 2022, Journal of Ambient Intelligence and Humanized Computing.

[4]  A. Gordon,et al.  Alarming antibody evasion properties of rising SARS-CoV-2 BQ and XBB subvariants , 2022, Cell.

[5]  Nooshan Sedaghati,et al.  Study of Metadata Impact on COVID-19 Detection using Convolutional Neural Networks , 2022, 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI).

[6]  R. Gupta,et al.  An AI-enabled pre-trained model-based Covid detection model using chest X-ray images , 2022, Multimedia Tools and Applications.

[7]  I. A. Moonesar,et al.  Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey , 2022, Frontiers in Artificial Intelligence.

[8]  Jaber S. Alzahrani,et al.  An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification , 2022, Healthcare.

[9]  D. Storman,et al.  Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review , 2022, Journal of clinical medicine.

[10]  D. Alsaeed,et al.  Brain MRI Analysis for Alzheimer’s Disease Diagnosis Using CNN-Based Feature Extraction and Machine Learning , 2022, Sensors.

[11]  A. Hamida,et al.  COVID-19 detection in CT and CXR images using deep learning models , 2022, Biogerontology.

[12]  Amad Zafar,et al.  Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model , 2022, Sensors.

[13]  M. Sawan,et al.  COVID-19 Diagnostic Methods and Detection Techniques , 2021, Reference Module in Biomedical Sciences.

[14]  W. Wang,et al.  D2-CovidNet: A Deep Learning Model for COVID-19 Detection in Chest X-Ray Images , 2021, Comput. Intell. Neurosci..

[15]  Sagar B. Amin,et al.  Detecting COVID-19 from chest computed tomography scans using AI-driven android application , 2021, Computers in Biology and Medicine.

[16]  J. Górriz,et al.  NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network , 2021, Int. J. Intell. Syst..

[17]  M. Sharif,et al.  Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network , 2021, Microscopy research and technique.

[18]  J. M. Saborit-Torres,et al.  COVID-19 detection in X-ray images using convolutional neural networks , 2021, Machine Learning with Applications.

[19]  R. Lu,et al.  Real-time reverse transcription-polymerase chain reaction assay panel for the detection of severe acute respiratory syndrome coronavirus 2 and its variants , 2021, Chinese medical journal.

[20]  S. Kadry,et al.  Quantum Machine Learning Architecture for COVID-19 Classification Based on Synthetic Data Generation Using Conditional Adversarial Neural Network , 2021, Cognitive Computation.

[21]  Jie Hou,et al.  Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection , 2021, Scientific Reports.

[22]  Amad Zafar,et al.  Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images , 2021, Sensors.

[23]  Marwa Ben Jabra,et al.  Deep learning based detection of COVID-19 from chest X-ray images , 2021, Multimedia Tools and Applications.

[24]  E. Lau,et al.  Clinical improvement, outcomes, antiviral activity, and costs associated with early treatment with remdesivir for patients with COVID-19 , 2021, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[25]  T. Pham,et al.  Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images , 2021, Multimedia Systems.

[26]  M. Sharif,et al.  An integrated framework for COVID‐19 classification based on classical and quantum transfer learning from a chest radiograph , 2021, Concurr. Comput. Pract. Exp..

[27]  Jeonghwan Gwak,et al.  MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers , 2021, Sensors.

[28]  G. Muhammad,et al.  COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images , 2021, Information Fusion.

[29]  Kate N. Wang,et al.  Automated detection of COVID-19 through convolutional neural network using chest x-ray images , 2021, medRxiv.

[30]  Dominique Duncan,et al.  Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT , 2020, Expert Systems with Applications.

[31]  S. Meshgini,et al.  A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images , 2020, 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME).

[32]  Deepak Ranjan Nayak,et al.  COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis , 2020, Information Fusion.

[33]  M. E. Karar,et al.  Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans , 2020, Complex & intelligent systems.

[34]  Eisa A. Alanazi,et al.  COVID-19 open source data sets: a comprehensive survey , 2020, Applied Intelligence.

[35]  Umut Ozkaya,et al.  Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods , 2020, RTA-CSIT.

[36]  Xiao Chen,et al.  Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning , 2020 .

[37]  Chao Yang,et al.  A Survey on Deep Transfer Learning , 2018, ICANN.

[38]  Muhammad Attique Khan,et al.  Classification of Positive COVID-19 CT Scans using Deep Learning , 2021 .

[39]  Xin Zhang,et al.  Detection of COVID-19 by GoogLeNet-COD , 2020, ICIC.

[40]  Kemal Adem,et al.  Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder , 2019, Expert Syst. Appl..