Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model
Khalaf Alshamrani,1,2 Hassan A Alshamrani1 1Radiological Sciences Department, Najran University, Najran, Saudi Arabia; 2Department of Oncology and Metabolism, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Department of Oncology and Metabolism, University of Sheffield, Shef...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2024-04-01
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| Series: | Journal of Multidisciplinary Healthcare |
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| Online Access: | https://www.dovepress.com/classification-of-chest-ct-lung-nodules-using-collaborative-deep-learn-peer-reviewed-fulltext-article-JMDH |
| _version_ | 1797222265424707584 |
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| author | Alshamrani K Alshamrani HA |
| author_facet | Alshamrani K Alshamrani HA |
| author_sort | Alshamrani K |
| collection | DOAJ |
| description | Khalaf Alshamrani,1,2 Hassan A Alshamrani1 1Radiological Sciences Department, Najran University, Najran, Saudi Arabia; 2Department of Oncology and Metabolism, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK, Email k.alshamrani@sheffield.ac.uk; kaalshamrani@hotmail.comBackground: Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets.Methods: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy.Results: The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach.Conclusion: The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.Keywords: CT images, lung cancer, nodules, logistic regression, collaborative deep learning, standard deviation, radial length |
| first_indexed | 2024-04-24T13:18:35Z |
| format | Article |
| id | doaj.art-ca26711d83bc4292b89b03e879008c13 |
| institution | Directory Open Access Journal |
| issn | 1178-2390 |
| language | English |
| last_indexed | 2024-04-24T13:18:35Z |
| publishDate | 2024-04-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Multidisciplinary Healthcare |
| spelling | doaj.art-ca26711d83bc4292b89b03e879008c132024-04-04T16:51:52ZengDove Medical PressJournal of Multidisciplinary Healthcare1178-23902024-04-01Volume 171459147291738Classification of Chest CT Lung Nodules Using Collaborative Deep Learning ModelAlshamrani KAlshamrani HAKhalaf Alshamrani,1,2 Hassan A Alshamrani1 1Radiological Sciences Department, Najran University, Najran, Saudi Arabia; 2Department of Oncology and Metabolism, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK, Email k.alshamrani@sheffield.ac.uk; kaalshamrani@hotmail.comBackground: Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets.Methods: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy.Results: The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach.Conclusion: The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.Keywords: CT images, lung cancer, nodules, logistic regression, collaborative deep learning, standard deviation, radial lengthhttps://www.dovepress.com/classification-of-chest-ct-lung-nodules-using-collaborative-deep-learn-peer-reviewed-fulltext-article-JMDHct imageslung cancernoduleslogistic regressioncollaborative deep learningstandard deviationand radial length. |
| spellingShingle | Alshamrani K Alshamrani HA Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model Journal of Multidisciplinary Healthcare ct images lung cancer nodules logistic regression collaborative deep learning standard deviation and radial length. |
| title | Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model |
| title_full | Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model |
| title_fullStr | Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model |
| title_full_unstemmed | Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model |
| title_short | Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model |
| title_sort | classification of chest ct lung nodules using collaborative deep learning model |
| topic | ct images lung cancer nodules logistic regression collaborative deep learning standard deviation and radial length. |
| url | https://www.dovepress.com/classification-of-chest-ct-lung-nodules-using-collaborative-deep-learn-peer-reviewed-fulltext-article-JMDH |
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