Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis.

Autor: Althubaity DD; Pediatric Nursing Department, Faculty of Nursing, Najran University, Najran 66441, Saudi Arabia., Alotaibi FF; Strategy Studies and Planning Department, Prince Sultan Medical Military City, Riyadh 13521, Saudi Arabia., Osman AMA; Community and Mental Health, College of Nursing, Najran University, Najran 66441, Saudi Arabia., Al-Khadher MA; Nursing College, Najran University, Najran 66441, Saudi Arabia., Abdalla YHA; Nursing College, Najran University, Najran 66441, Saudi Arabia., Alwesabi SA; Nursing College, Najran University, Najran 66441, Saudi Arabia., Abdulrahman EEH; Nursing College, Najran University, Najran 66441, Saudi Arabia., Alhemairy MA; Nursing College, Najran University, Najran 66441, Saudi Arabia.
Jazyk: angličtina
Zdroj: Journal of personalized medicine [J Pers Med] 2023 Feb 23; Vol. 13 (3). Date of Electronic Publication: 2023 Feb 23.
DOI: 10.3390/jpm13030388
Abstrakt: 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.
Databáze: MEDLINE
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