Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis
DaifAllah D Al-Thubaity, Faisal Fahad Alotaibi, Abdalla Mohamed Ahmed Osman, Mugahed Ali Alkhadher, Yahya Hussein Ahmed Abdalla, Sadeq Abdo Mohammed Alwesabi, Elsadig Eltaher Hamed Abdulrahman, Maram Abdulkhalek Alhemairy · Journal of Personalized Medicine · 2023
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.