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Ahmad IS, Dai J, Xie Y, Liang X. Deep learning models for CT image classification: a comprehensive literature review. Quant Imaging Med Surg 2025; 15:962-1011. [PMID: 39838987 PMCID: PMC11744119 DOI: 10.21037/qims-24-1400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 10/18/2024] [Indexed: 01/23/2025]
Abstract
Background and Objective Computed tomography (CT) imaging plays a crucial role in the early detection and diagnosis of life-threatening diseases, particularly in respiratory illnesses and oncology. The rapid advancement of deep learning (DL) has revolutionized CT image analysis, enhancing diagnostic accuracy and efficiency. This review explores the impact of advanced DL methodologies in CT imaging, with a particular focus on their applications in coronavirus disease 2019 (COVID-19) detection and lung nodule classification. Methods A comprehensive literature search was conducted, examining the evolution of DL architectures in medical imaging from conventional convolutional neural networks (CNNs) to sophisticated foundational models (FMs). We reviewed publications from major databases, focusing on developments in CT image analysis using DL from 2013 to 2023. Our search criteria included all types of articles, with a focus on peer-reviewed research papers and review articles in English. Key Content and Findings The review reveals that DL, particularly advanced architectures like FMs, has transformed CT image analysis by streamlining interpretation processes and enhancing diagnostic capabilities. We found significant advancements in addressing global health challenges, especially during the COVID-19 pandemic, and in ongoing efforts for lung cancer screening. The review also addresses technical challenges in CT image analysis, including data variability, the need for large high-quality datasets, and computational demands. Innovative strategies such as transfer learning, data augmentation, and distributed computing are explored as solutions to these challenges. Conclusions This review underscores the pivotal role of DL in advancing CT image analysis, particularly for COVID-19 and lung nodule detection. The integration of DL models into clinical workflows shows promising potential to enhance diagnostic accuracy and efficiency. However, challenges remain in areas of interpretability, validation, and regulatory compliance. The review advocates for continued research, interdisciplinary collaboration, and ethical considerations as DL technologies become integral to clinical practice. While traditional imaging techniques remain vital, the integration of DL represents a significant advancement in medical diagnostics, with far-reaching implications for future research, clinical practice, and healthcare policy.
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Affiliation(s)
- Isah Salim Ahmad
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingjing Dai
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
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Piffer S, Ubaldi L, Tangaro S, Retico A, Talamonti C. Tackling the small data problem in medical image classification with artificial intelligence: a systematic review. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2024; 6:032001. [PMID: 39655846 DOI: 10.1088/2516-1091/ad525b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/30/2024] [Indexed: 12/18/2024]
Abstract
Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting. 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyse (PRISMA) guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). To solve the small data issue transfer learning technique, basic data augmentation and generative adversarial network were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only four studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items). The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.
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Affiliation(s)
- Stefano Piffer
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy
| | - Leonardo Ubaldi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy
| | - Sabina Tangaro
- Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Bari, Italy
- INFN, Bari Division, Bari, Italy
| | | | - Cinzia Talamonti
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- National Institute for Nuclear Physics (INFN), Florence Division, Florence, Italy
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Ozaltin O, Yeniay O, Subasi A. OzNet: A New Deep Learning Approach for Automated Classification of COVID-19 Computed Tomography Scans. BIG DATA 2023; 11:420-436. [PMID: 36927081 DOI: 10.1089/big.2022.0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Therefore, the classification of computed tomography (CT) scans alleviates the workload of experts, whose workload increased considerably during the pandemic. Convolutional neural network (CNN) architectures are successful for the classification of medical images. In this study, we have developed a new deep CNN architecture called OzNet. Moreover, we have compared it with pretrained architectures namely AlexNet, DenseNet201, GoogleNet, NASNetMobile, ResNet-50, SqueezeNet, and VGG-16. In addition, we have compared the classification success of three preprocessing methods with raw CT scans. We have not only classified the raw CT scans, but also have performed the classification with three different preprocessing methods, which are discrete wavelet transform (DWT), intensity adjustment, and gray to color red, green, blue image conversion on the data sets. Furthermore, it is known that the architecture's performance increases with the use of DWT preprocessing method rather than using the raw data set. The results are extremely promising with the CNN algorithms using the COVID-19 CT scans processed with the DWT. The proposed DWT-OzNet has achieved a high classification performance of more than 98.8% for each calculated metric.
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Affiliation(s)
- Oznur Ozaltin
- Department of Statistics, Institute of Science, Hacettepe University, Ankara, Turkey
| | - Ozgur Yeniay
- Department of Statistics, Institute of Science, Hacettepe University, Ankara, Turkey
| | - Abdulhamit Subasi
- Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
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4
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Jiang X, Yu J, Ye J, Jia W, Xu W, Shu Q. A deep learning-based method for pediatric congenital heart disease detection with seven standard views in echocardiography. WORLD JOURNAL OF PEDIATRIC SURGERY 2023; 6:e000580. [PMID: 37303480 PMCID: PMC10255206 DOI: 10.1136/wjps-2023-000580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/26/2023] [Indexed: 06/13/2023] Open
Abstract
Background With the aggregation of clinical data and the evolution of computational resources, artificial intelligence-based methods have become possible to facilitate clinical diagnosis. For congenital heart disease (CHD) detection, recent deep learning-based methods tend to achieve classification with few views or even a single view. Due to the complexity of CHD, the input images for the deep learning model should cover as many anatomical structures of the heart as possible to enhance the accuracy and robustness of the algorithm. In this paper, we first propose a deep learning method based on seven views for CHD classification and then validate it with clinical data, the results of which show the competitiveness of our approach. Methods A total of 1411 children admitted to the Children's Hospital of Zhejiang University School of Medicine were selected, and their echocardiographic videos were obtained. Then, seven standard views were selected from each video, which were used as the input to the deep learning model to obtain the final result after training, validation and testing. Results In the test set, when a reasonable type of image was input, the area under the curve (AUC) value could reach 0.91, and the accuracy could reach 92.3%. During the experiment, shear transformation was used as interference to test the infection resistance of our method. As long as appropriate data were input, the above experimental results would not fluctuate obviously even if artificial interference was applied. Conclusions These results indicate that the deep learning model based on the seven standard echocardiographic views can effectively detect CHD in children, and this approach has considerable value in practical application.
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Affiliation(s)
- Xusheng Jiang
- Department of Cardiac Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Jin Yu
- Department of Ultrasound Diagnosis, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Jingjing Ye
- Department of Ultrasound Diagnosis, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Weijie Jia
- Innovation Center for Child Health, Binjiang Institute of Zhejiang University, Hangzhou, China
| | - Weize Xu
- Department of Cardiac Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Qiang Shu
- Department of Cardiac Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
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Esmi N, Golshan Y, Asadi S, Shahbahrami A, Gaydadjiev G. A fuzzy fine-tuned model for COVID-19 diagnosis. Comput Biol Med 2023; 153:106483. [PMID: 36621192 PMCID: PMC9811914 DOI: 10.1016/j.compbiomed.2022.106483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 12/25/2022] [Indexed: 01/06/2023]
Abstract
The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.
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Affiliation(s)
- Nima Esmi
- Faculty of Science and Engineering, University of Groningen, Netherlands.
| | - Yasaman Golshan
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Sara Asadi
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Asadollah Shahbahrami
- Faculty of Science and Engineering, University of Groningen, Netherlands; Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Georgi Gaydadjiev
- Faculty of Science and Engineering, University of Groningen, Netherlands.
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Kathamuthu ND, Subramaniam S, Le QH, Muthusamy S, Panchal H, Sundararajan SCM, Alrubaie AJ, Zahra MMA. A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. ADVANCES IN ENGINEERING SOFTWARE (BARKING, LONDON, ENGLAND : 1992) 2023; 175:103317. [PMID: 36311489 PMCID: PMC9595382 DOI: 10.1016/j.advengsoft.2022.103317] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/09/2022] [Accepted: 10/19/2022] [Indexed: 05/26/2023]
Abstract
The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.
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Affiliation(s)
- Nirmala Devi Kathamuthu
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Shanthi Subramaniam
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Quynh Hoang Le
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
| | - Suresh Muthusamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India
| | - Hitesh Panchal
- Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India
| | | | - Ali Jawad Alrubaie
- Department of Medical Instrumentation Techniques Engineering, Al- Mustaqbal University College, 51001, Hilla, Iraq
| | - Musaddak Maher Abdul Zahra
- Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq
- Electrical Engineering Department, College of Engineering, University of Babylon, Hilla, Babil, Iraq
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7
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Gomes R, Kamrowski C, Langlois J, Rozario P, Dircks I, Grottodden K, Martinez M, Tee WZ, Sargeant K, LaFleur C, Haley M. A Comprehensive Review of Machine Learning Used to Combat COVID-19. Diagnostics (Basel) 2022; 12:1853. [PMID: 36010204 PMCID: PMC9406981 DOI: 10.3390/diagnostics12081853] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 12/19/2022] Open
Abstract
Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.
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Affiliation(s)
- Rahul Gomes
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Connor Kamrowski
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Jordan Langlois
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Papia Rozario
- Department of Geography and Anthropology, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA;
| | - Ian Dircks
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Keegan Grottodden
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Matthew Martinez
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Wei Zhong Tee
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Kyle Sargeant
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Corbin LaFleur
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
| | - Mitchell Haley
- Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA; (C.K.); (J.L.); (I.D.); (K.G.); (M.M.); (W.Z.T.); (K.S.); (C.L.); (M.H.)
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