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Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of
DaifAllah D Althubaity1, Faisal Fahad Alotaibi2, Abdalla Mohamed Ahmed Osman3
1Pediatric Nursing Department, Faculty of Nursing, Najran University, Najran 66441, Saudi Arabia.
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.
