Overview

Lung cancer diagnosis is a pressing concern within global health, necessitating effective methodologies for enhanced patient care. This study explores the integration of the Mask Region Convolutional Neural Network (Mask R-CNN) for diagnosing lung cancer. This study applies Mask R-CNN to the challenge of diagnosing lung cancer in Kurdistan. The methodology involves assembling a dataset tailored to Kurdish lung cancer characteristics. The Mask R-CNN is trained on this dataset, focusing on parameter optimization. The outcomes reveal improved lung cancer detection precision compared to conventional methods. This research uncovers nuances in lung cancer presentations within Kurdistan. Analyzing cancerous region patterns could reveal correlations with genetic or environmental factors, refining diagnostic protocols and informing personalized interventions. The implications extend beyond application, hinting at technology-healthcare synergy. Mask R-CNN's pixel-level segmentation introduces precision interventions.In conclusion, applying Mask R-CNN to lung cancer diagnosis in Kurdistan merges technology with healthcare imperatives.

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