Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis
Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Dev...
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MDPI AG
2023-02-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/13/3/388 |
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| author | DaifAllah D. Althubaity Faisal Fahad Alotaibi Abdalla Mohamed Ahmed Osman Mugahed Ali Al-khadher Yahya Hussein Ahmed Abdalla Sadeq Abdo Alwesabi Elsadig Eltaher Hamed Abdulrahman Maram Abdulkhalek Alhemairy |
| author_facet | DaifAllah D. Althubaity Faisal Fahad Alotaibi Abdalla Mohamed Ahmed Osman Mugahed Ali Al-khadher Yahya Hussein Ahmed Abdalla Sadeq Abdo Alwesabi Elsadig Eltaher Hamed Abdulrahman Maram Abdulkhalek Alhemairy |
| author_sort | DaifAllah D. Althubaity |
| collection | DOAJ |
| description | Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. Results: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. Conclusion: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer. |
| first_indexed | 2024-03-11T06:19:55Z |
| format | Article |
| id | doaj.art-339bf23434e64d10ac90867c5f20bf3a |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-11T06:19:55Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-339bf23434e64d10ac90867c5f20bf3a2023-11-17T12:01:38ZengMDPI AGJournal of Personalized Medicine2075-44262023-02-0113338810.3390/jpm13030388Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted DiagnosisDaifAllah D. Althubaity0Faisal Fahad Alotaibi1Abdalla Mohamed Ahmed Osman2Mugahed Ali Al-khadher3Yahya Hussein Ahmed Abdalla4Sadeq Abdo Alwesabi5Elsadig Eltaher Hamed Abdulrahman6Maram Abdulkhalek Alhemairy7Pediatric Nursing Department, Faculty of Nursing, Najran University, Najran 66441, Saudi ArabiaStrategy Studies and Planning Department, Prince Sultan Medical Military City, Riyadh 13521, Saudi ArabiaCommunity and Mental Health, College of Nursing, Najran University, Najran 66441, Saudi ArabiaNursing College, Najran University, Najran 66441, Saudi ArabiaNursing College, Najran University, Najran 66441, Saudi ArabiaNursing College, Najran University, Najran 66441, Saudi ArabiaNursing College, Najran University, Najran 66441, Saudi ArabiaNursing College, Najran University, Najran 66441, Saudi ArabiaBackground: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. Results: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. Conclusion: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.https://www.mdpi.com/2075-4426/13/3/388lung cancerautomatic identificationTMACADTumorhistopathological images |
| spellingShingle | DaifAllah D. Althubaity Faisal Fahad Alotaibi Abdalla Mohamed Ahmed Osman Mugahed Ali Al-khadher Yahya Hussein Ahmed Abdalla Sadeq Abdo Alwesabi Elsadig Eltaher Hamed Abdulrahman Maram Abdulkhalek Alhemairy Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis Journal of Personalized Medicine lung cancer automatic identification TMA CAD Tumor histopathological images |
| title | Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis |
| title_full | Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis |
| title_fullStr | Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis |
| title_full_unstemmed | Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis |
| title_short | Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis |
| title_sort | automated lung cancer segmentation in tissue micro array analysis histopathological images using a prototype of computer assisted diagnosis |
| topic | lung cancer automatic identification TMA CAD Tumor histopathological images |
| url | https://www.mdpi.com/2075-4426/13/3/388 |
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