Lung Diseases Diagnosis-Based Deep Learning Methods: A Review
This review paper examines the current state of lung disease diagnosis based on deep learning (DL) methods. Lung diseases, such as Pneumonia, TB, Covid-19, and lung cancer, are significant causes of morbidity and mortality worldwide. Accurate and timely diagnosis of these diseases is essential for...
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| Language: | English |
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middle technical university
2023-09-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/1469 |
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| author | Shahad A. Salih Sadik Kamel Gharghan Jinan F. Mahdi Inas Jawad Kadhim |
| author_facet | Shahad A. Salih Sadik Kamel Gharghan Jinan F. Mahdi Inas Jawad Kadhim |
| author_sort | Shahad A. Salih |
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This review paper examines the current state of lung disease diagnosis based on deep learning (DL) methods. Lung diseases, such as Pneumonia, TB, Covid-19, and lung cancer, are significant causes of morbidity and mortality worldwide. Accurate and timely diagnosis of these diseases is essential for effective treatment and improved patient outcomes. DL methods, which utilize artificial neural networks to extract features from medical images automatically, have shown great promise in improving the accuracy and efficiency of lung disease diagnosis. This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). The advantages and limitations of each method are discussed, along with the types of medical imaging techniques used, such as X-ray and computed tomography (CT). In addition, the review discusses the most commonly used performance metrics for evaluating the performance of DL for lung disease diagnosis: the area under the curve (AUC), sensitivity, specificity, F1-score, accuracy, precision, and the receiver operator characteristic curve (ROC). Moreover, the challenges and limitations of using DL for lung disease diagnosis, including the limited availability of annotated data, the variability in imaging techniques and disease presentation, and the interpretability and generalizability of DL models, are highlighted in this paper. Furthermore, strategies to overcome these challenges, such as transfer learning, data augmentation, and explainable AI, are also discussed. The review concludes with a call for further research to address the remaining challenges and realize DL's full potential for improving lung disease diagnosis and treatment.
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| format | Article |
| id | doaj-art-373ab2a5cfbc43edac3e93e43d6cf3e7 |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | middle technical university |
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| series | Journal of Techniques |
| spelling | doaj-art-373ab2a5cfbc43edac3e93e43d6cf3e72025-01-19T10:59:04Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-09-015310.51173/jt.v5i3.1469Lung Diseases Diagnosis-Based Deep Learning Methods: A ReviewShahad A. Salih0Sadik Kamel Gharghan1Jinan F. Mahdi2Inas Jawad Kadhim3Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq. This review paper examines the current state of lung disease diagnosis based on deep learning (DL) methods. Lung diseases, such as Pneumonia, TB, Covid-19, and lung cancer, are significant causes of morbidity and mortality worldwide. Accurate and timely diagnosis of these diseases is essential for effective treatment and improved patient outcomes. DL methods, which utilize artificial neural networks to extract features from medical images automatically, have shown great promise in improving the accuracy and efficiency of lung disease diagnosis. This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). The advantages and limitations of each method are discussed, along with the types of medical imaging techniques used, such as X-ray and computed tomography (CT). In addition, the review discusses the most commonly used performance metrics for evaluating the performance of DL for lung disease diagnosis: the area under the curve (AUC), sensitivity, specificity, F1-score, accuracy, precision, and the receiver operator characteristic curve (ROC). Moreover, the challenges and limitations of using DL for lung disease diagnosis, including the limited availability of annotated data, the variability in imaging techniques and disease presentation, and the interpretability and generalizability of DL models, are highlighted in this paper. Furthermore, strategies to overcome these challenges, such as transfer learning, data augmentation, and explainable AI, are also discussed. The review concludes with a call for further research to address the remaining challenges and realize DL's full potential for improving lung disease diagnosis and treatment. https://journal.mtu.edu.iq/index.php/MTU/article/view/1469COVID-19CT ScanDeep LearningImage ProcessingLung CancerPneumonia |
| spellingShingle | Shahad A. Salih Sadik Kamel Gharghan Jinan F. Mahdi Inas Jawad Kadhim Lung Diseases Diagnosis-Based Deep Learning Methods: A Review Journal of Techniques COVID-19 CT Scan Deep Learning Image Processing Lung Cancer Pneumonia |
| title | Lung Diseases Diagnosis-Based Deep Learning Methods: A Review |
| title_full | Lung Diseases Diagnosis-Based Deep Learning Methods: A Review |
| title_fullStr | Lung Diseases Diagnosis-Based Deep Learning Methods: A Review |
| title_full_unstemmed | Lung Diseases Diagnosis-Based Deep Learning Methods: A Review |
| title_short | Lung Diseases Diagnosis-Based Deep Learning Methods: A Review |
| title_sort | lung diseases diagnosis based deep learning methods a review |
| topic | COVID-19 CT Scan Deep Learning Image Processing Lung Cancer Pneumonia |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/1469 |
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