An Efficient Dual-Sampling Approach for Chest CT Diagnosis
Khalaf Alshamrani,1,2 Hassan A Alshamrani1 1Radiology Sciences Department, College of Medical Sciences, Najran University, Najran, Saudi Arabia; 2School of Medicine and Population Health, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Email Kaalshamrani@nu.edu.sa, k.alshamr...
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Dove Medical Press
2025-01-01
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| Series: | Journal of Multidisciplinary Healthcare |
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| Online Access: | https://www.dovepress.com/an-efficient-dual-sampling-approach-for-chest-ct-diagnosis-peer-reviewed-fulltext-article-JMDH |
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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 1Radiology Sciences Department, College of Medical Sciences, Najran University, Najran, Saudi Arabia; 2School of Medicine and Population Health, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Email Kaalshamrani@nu.edu.sa, k.alshamrani@sheffield.ac.ukBackground: This paper aimed to enhance the diagnostic process of lung abnormalities in computed tomography (CT) images, particularly in distinguishing cancer cells from normal chest tissue. The rapid and uneven growth of cancer cells, presenting with variable symptoms, necessitates an advanced approach for accurate identification.Objective: To develop a dual-sampling network targeting lung infection regions to address the diagnostic challenge. The network was designed to adapt to the uneven distribution of infection areas, which could be predominantly minor or major in different regions.Methods: A total of 150 CT images were analyzed using the dual-sampling network. Two sampling approaches were compared: the proposed dual-sampling technique and a uniform sampling method.Results: The dual-sampling network demonstrated superior performance in detecting lung abnormalities compared to uniform sampling. The uniform sampling method, the network results: an F1-Score of 94.2%, accuracy of 94.5%, sensitivity of 93.5%, specificity of 95.4%, and an area under the curve (AUC) of 98.4%. However, with the proposed dual-sampling method, the network reached an F1-score of 94.9%, accuracy of 95.2%, specificity of 96.1%, sensitivity of 94.2%, and an AUC of 95.5%.Conclusion: This study suggests that the proposed dual-sampling network significantly improves the precision of lung abnormality diagnosis in CT images. This advancement has the potential to aid radiologists in making more accurate diagnoses, ultimately benefiting patient treatment and contributing to better overall population health. The efficiency and effectiveness of the dual-sampling approach in managing the uneven distribution of lung infection areas are key to its success.Keywords: lung cancer, SVM, KNN, dual sampling, uniform sampling, balanced class sampling, under sampling and over fitting |
| format | Article |
| id | doaj-art-e15207ad0a78486f8a628cd60f295027 |
| institution | Kabale University |
| issn | 1178-2390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Multidisciplinary Healthcare |
| spelling | doaj-art-e15207ad0a78486f8a628cd60f2950272025-01-19T16:42:59ZengDove Medical PressJournal of Multidisciplinary Healthcare1178-23902025-01-01Volume 1823925399336An Efficient Dual-Sampling Approach for Chest CT DiagnosisAlshamrani KAlshamrani HAKhalaf Alshamrani,1,2 Hassan A Alshamrani1 1Radiology Sciences Department, College of Medical Sciences, Najran University, Najran, Saudi Arabia; 2School of Medicine and Population Health, University of Sheffield, Sheffield, UKCorrespondence: Khalaf Alshamrani, Email Kaalshamrani@nu.edu.sa, k.alshamrani@sheffield.ac.ukBackground: This paper aimed to enhance the diagnostic process of lung abnormalities in computed tomography (CT) images, particularly in distinguishing cancer cells from normal chest tissue. The rapid and uneven growth of cancer cells, presenting with variable symptoms, necessitates an advanced approach for accurate identification.Objective: To develop a dual-sampling network targeting lung infection regions to address the diagnostic challenge. The network was designed to adapt to the uneven distribution of infection areas, which could be predominantly minor or major in different regions.Methods: A total of 150 CT images were analyzed using the dual-sampling network. Two sampling approaches were compared: the proposed dual-sampling technique and a uniform sampling method.Results: The dual-sampling network demonstrated superior performance in detecting lung abnormalities compared to uniform sampling. The uniform sampling method, the network results: an F1-Score of 94.2%, accuracy of 94.5%, sensitivity of 93.5%, specificity of 95.4%, and an area under the curve (AUC) of 98.4%. However, with the proposed dual-sampling method, the network reached an F1-score of 94.9%, accuracy of 95.2%, specificity of 96.1%, sensitivity of 94.2%, and an AUC of 95.5%.Conclusion: This study suggests that the proposed dual-sampling network significantly improves the precision of lung abnormality diagnosis in CT images. This advancement has the potential to aid radiologists in making more accurate diagnoses, ultimately benefiting patient treatment and contributing to better overall population health. The efficiency and effectiveness of the dual-sampling approach in managing the uneven distribution of lung infection areas are key to its success.Keywords: lung cancer, SVM, KNN, dual sampling, uniform sampling, balanced class sampling, under sampling and over fittinghttps://www.dovepress.com/an-efficient-dual-sampling-approach-for-chest-ct-diagnosis-peer-reviewed-fulltext-article-JMDHlung cancersvmknndual samplinguniform samplingbalanced class samplingunder sampling and over fitting. |
| spellingShingle | Alshamrani K Alshamrani HA An Efficient Dual-Sampling Approach for Chest CT Diagnosis Journal of Multidisciplinary Healthcare lung cancer svm knn dual sampling uniform sampling balanced class sampling under sampling and over fitting. |
| title | An Efficient Dual-Sampling Approach for Chest CT Diagnosis |
| title_full | An Efficient Dual-Sampling Approach for Chest CT Diagnosis |
| title_fullStr | An Efficient Dual-Sampling Approach for Chest CT Diagnosis |
| title_full_unstemmed | An Efficient Dual-Sampling Approach for Chest CT Diagnosis |
| title_short | An Efficient Dual-Sampling Approach for Chest CT Diagnosis |
| title_sort | efficient dual sampling approach for chest ct diagnosis |
| topic | lung cancer svm knn dual sampling uniform sampling balanced class sampling under sampling and over fitting. |
| url | https://www.dovepress.com/an-efficient-dual-sampling-approach-for-chest-ct-diagnosis-peer-reviewed-fulltext-article-JMDH |
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