1
|
Rana S, Hosen MJ, Tonni TJ, Rony MAH, Fatema K, Hasan MZ, Rahman MT, Khan RT, Jan T, Whaiduzzaman M. DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features. SENSORS (BASEL, SWITZERLAND) 2024; 24:2830. [PMID: 38732936 PMCID: PMC11086108 DOI: 10.3390/s24092830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/06/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.
Collapse
Affiliation(s)
- Shakil Rana
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md Jabed Hosen
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Tasnim Jahan Tonni
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md. Awlad Hossen Rony
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Kaniz Fatema
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md. Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md. Tanvir Rahman
- School of Health and Rehabilitation Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
| | - Risala Tasin Khan
- Institute of Information Technology, Jahangirnagar University, Dhaka 1342, Bangladesh;
| | - Tony Jan
- Centre for Artificial Intelligence Research and Optimisation (AIRO), Torrens University, Ultimo, NSW 2007, Australia;
| | - Md Whaiduzzaman
- Centre for Artificial Intelligence Research and Optimisation (AIRO), Torrens University, Ultimo, NSW 2007, Australia;
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
| |
Collapse
|
2
|
Chetoui M, Akhloufi MA, Bouattane EM, Abdulnour J, Roux S, Bernard CD. Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture. Viruses 2023; 15:1327. [PMID: 37376626 DOI: 10.3390/v15061327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays. The recent deep convolutional neural network (CNN) RegNetX032 was adapted for detecting COVID-19 from chest X-ray (CXR) images using polymerase chain reaction (RT-PCR) as a reference. The model was customized and trained on five datasets containing more than 15,000 CXR images (including 4148COVID-19-positive cases) and then tested on 321 images (150 COVID-19-positive) from Montfort Hospital. Twenty percent of the data from the five datasets were used as validation data for hyperparameter optimization. Each CXR image was processed by the model to detect COVID-19. Multi-binary classifications were proposed, such as: COVID-19 vs. normal, COVID-19 + pneumonia vs. normal, and pneumonia vs. normal. The performance results were based on the area under the curve (AUC), sensitivity, and specificity. In addition, an explainability model was developed that demonstrated the high performance and high generalization degree of the proposed model in detecting and highlighting the signs of the disease. The fine-tuned RegNetX032 model achieved an overall accuracy score of 96.0%, with an AUC score of 99.1%. The model showed a superior sensitivity of 98.0% in detecting signs from CXR images of COVID-19 patients, and a specificity of 93.0% in detecting healthy CXR images. A second scenario compared COVID-19 + pneumonia vs. normal (healthy X-ray) patients. The model achieved an overall score of 99.1% (AUC) with a sensitivity of 96.0% and specificity of 93.0% on the Montfort dataset. For the validation set, the model achieved an average accuracy of 98.6%, an AUC score of 98.0%, a sensitivity of 98.0%, and a specificity of 96.0% for detection (COVID-19 patients vs. healthy patients). The second scenario compared COVID-19 + pneumonia vs. normal patients. The model achieved an overall score of 98.8% (AUC) with a sensitivity of 97.0% and a specificity of 96.0%. This robust deep learning model demonstrated excellent performance in detecting COVID-19 from chest X-rays. This model could be used to automate the detection of COVID-19 and improve decision making for patient triage and isolation in hospital settings. This could also be used as a complementary aid for radiologists or clinicians when differentiating to make smart decisions.
Collapse
Affiliation(s)
- Mohamed Chetoui
- Perception, Robotics, and Intelligent Machines (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada
| | - Moulay A Akhloufi
- Perception, Robotics, and Intelligent Machines (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada
| | - El Mostafa Bouattane
- Montfort Academic Hospital, Institut du Savoir Montfort, Ottawa, ON 61350, Canada
| | - Joseph Abdulnour
- Montfort Academic Hospital, Institut du Savoir Montfort, Ottawa, ON 61350, Canada
| | - Stephane Roux
- Montfort Academic Hospital, Institut du Savoir Montfort, Ottawa, ON 61350, Canada
| | | |
Collapse
|
3
|
Hamad QS, Samma H, Suandi SA. Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study. APPL INTELL 2023; 53:1-23. [PMID: 36777882 PMCID: PMC9900578 DOI: 10.1007/s10489-022-04446-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/29/2022] [Indexed: 02/08/2023]
Abstract
According to the World Health Organization, millions of infections and a lot of deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Since 2020, a lot of computer science researchers have used convolutional neural networks (CNNs) to develop interesting frameworks to detect this disease. However, poor feature extraction from the chest X-ray images and the high computational cost of the available models introduce difficulties for an accurate and fast COVID-19 detection framework. Moreover, poor feature extraction has caused the issue of 'the curse of dimensionality', which will negatively affect the performance of the model. Feature selection is typically considered as a preprocessing mechanism to find an optimal subset of features from a given set of all features in the data mining process. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier approaches. To achieve the specified goal, we design a mechanism for feature extraction based on shallow conventional neural network (SCNN) and used an effective method for selecting features by utilizing the newly developed optimization algorithm, Q-Learning Embedded Sine Cosine Algorithm (QLESCA). Support vector machines (SVMs) are used as a classifier. Five publicly available chest X-ray image datasets, consisting of 4848 COVID-19 images and 8669 non-COVID-19 images, are used to train and evaluate the proposed model. The performance of the QLESCA is evaluated against nine recent optimization algorithms. The proposed method is able to achieve the highest accuracy of 97.8086% while reducing the number of features from 100 to 38. Experiments prove that the accuracy of the model improves with the usage of the QLESCA as the dimensionality reduction technique by selecting relevant features. Graphical abstract
Collapse
Affiliation(s)
- Qusay Shihab Hamad
- Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia
- University of Information Technology and Communications (UOITC), Baghdad, Iraq
| | - Hussein Samma
- SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRC-AI), King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Shahrel Azmin Suandi
- Intelligent Biometric Group, School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia
| |
Collapse
|