Dynamic learning for imbalanced data in learning chest X-ray and CT images
Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the datasets are quite unbalanced in this situation, w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023040148 |
| _version_ | 1797812004141924352 |
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| author | Saeed Iqbal Adnan N. Qureshi Jianqiang Li Imran Arshad Choudhry Tariq Mahmood |
| author_facet | Saeed Iqbal Adnan N. Qureshi Jianqiang Li Imran Arshad Choudhry Tariq Mahmood |
| author_sort | Saeed Iqbal |
| collection | DOAJ |
| description | Massive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the datasets are quite unbalanced in this situation, with limited findings coming from significant instances of the novel illness. We offer a technique that allows a class balancing algorithm to understand and detect lung disease signs from chest X-ray and CT images. Deep learning techniques are used to train and evaluate images, enabling the extraction of basic visual attributes. The training objects' characteristics, instances, categories, and relative data modeling are all represented probabilistically. It is possible to identify a minority category in the classification process by using an imbalance-based sample analyzer. In order to address the imbalance problem, learning samples from the minority class are examined. The Support Vector Machine (SVM) is used to categorize images in clustering. Physicians and medical professionals can use the CNN model to validate their initial assessments of malignant and benign categorization. The proposed technique for class imbalance (3-Phase Dynamic Learning (3PDL)) and parallel CNN model (Hybrid Feature Fusion (HFF)) for multiple modalities achieve a high F1 score of 96.83 and precision is 96.87, its outstanding accuracy and generalization suggest that it may be utilized to create a pathologist's help tool. |
| first_indexed | 2024-03-13T07:32:08Z |
| format | Article |
| id | doaj.art-08d07b45a6794cba9b4662cb14208f46 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-13T07:32:08Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-08d07b45a6794cba9b4662cb14208f462023-06-04T04:24:14ZengElsevierHeliyon2405-84402023-06-0196e16807Dynamic learning for imbalanced data in learning chest X-ray and CT imagesSaeed Iqbal0Adnan N. Qureshi1Jianqiang Li2Imran Arshad Choudhry3Tariq Mahmood4Faculty of Information Technology, Beijing University of Technology, Beijing, 100124,China; Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Pakistan; Corresponding authors.Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, PakistanFaculty of Information Technology, Beijing University of Technology, Beijing, 100124,China; Beijing Engineering Research Center for IoT Software and Systems, 100124, China; Corresponding authors.Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, PakistanFaculty of Information Sciences, University of Education, Vehari Campus, Vehari, 61100, Pakistan; Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586, Kingdom of Saudi ArabiaMassive annotated datasets are necessary for networks of deep learning. When a topic is being researched for the first time, as in the situation of the viral epidemic, handling it with limited annotated datasets might be difficult. Additionally, the datasets are quite unbalanced in this situation, with limited findings coming from significant instances of the novel illness. We offer a technique that allows a class balancing algorithm to understand and detect lung disease signs from chest X-ray and CT images. Deep learning techniques are used to train and evaluate images, enabling the extraction of basic visual attributes. The training objects' characteristics, instances, categories, and relative data modeling are all represented probabilistically. It is possible to identify a minority category in the classification process by using an imbalance-based sample analyzer. In order to address the imbalance problem, learning samples from the minority class are examined. The Support Vector Machine (SVM) is used to categorize images in clustering. Physicians and medical professionals can use the CNN model to validate their initial assessments of malignant and benign categorization. The proposed technique for class imbalance (3-Phase Dynamic Learning (3PDL)) and parallel CNN model (Hybrid Feature Fusion (HFF)) for multiple modalities achieve a high F1 score of 96.83 and precision is 96.87, its outstanding accuracy and generalization suggest that it may be utilized to create a pathologist's help tool.http://www.sciencedirect.com/science/article/pii/S2405844023040148Class imbalanceRandom samplingDynamic learningFeature fusionEnsemble learningConvolutional neural network |
| spellingShingle | Saeed Iqbal Adnan N. Qureshi Jianqiang Li Imran Arshad Choudhry Tariq Mahmood Dynamic learning for imbalanced data in learning chest X-ray and CT images Heliyon Class imbalance Random sampling Dynamic learning Feature fusion Ensemble learning Convolutional neural network |
| title | Dynamic learning for imbalanced data in learning chest X-ray and CT images |
| title_full | Dynamic learning for imbalanced data in learning chest X-ray and CT images |
| title_fullStr | Dynamic learning for imbalanced data in learning chest X-ray and CT images |
| title_full_unstemmed | Dynamic learning for imbalanced data in learning chest X-ray and CT images |
| title_short | Dynamic learning for imbalanced data in learning chest X-ray and CT images |
| title_sort | dynamic learning for imbalanced data in learning chest x ray and ct images |
| topic | Class imbalance Random sampling Dynamic learning Feature fusion Ensemble learning Convolutional neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023040148 |
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