Optimizing Computed Tomography Image Reconstruction Parameters for Improved Lung Cancer Diagnosis with Grey Wolf Algorithm
Keywords:
Optimization, Lung Cancer Diagnosis, Image reconstruction, Disease diagnosisAbstract
Worldwide, lung cancer continues to be the most common cause of cancer-related mortality, necessitating ongoing efforts to improve early detection and diagnosis. In this context, computed tomography (CT) imaging is crucial because it makes it possible to see minute pulmonary abnormalities. The quality and diagnostic efficacy of CT scans are substantially impacted by image reconstruction parameters. In order to enhance lung cancer diagnosis, this work proposes a novel method for optimising these parameters utilising the Grey Wolf Algorithm (GWA).The GWA is highly suited for optimisation problems and is inspired by the social dynamics and hunting behaviour of grey wolves. In our study, the kernel selection, filter type, and exposure settings are only a few of the crucial CT image reconstruction factors that we fine-tune using the GWA. The suggested methodology seeks to balance edge preservation with picture noise reduction, ultimately improving the visibility of tiny lung lesions.We carried out extensive trials with a broad dataset of CT images from lung cancer patients to assess the efficacy of our method. Our findings show a considerable improvement in image quality, with less noise and more visible structural details. The better radiologist performance in identifying pulmonary nodules and lesions as a result of the optimised images ultimately increased the precision of lung cancer diagnosis.The GWA-based optimisation strategy also has a number of benefits, such as flexibility to different CT scanner models and robustness in dealing with varying patient demographics. This study emphasises the Grey Wolf Algorithm's potential as a useful tool for enhancing CT image reconstruction parameters and assisting in the early and precise identification of lung cancer, which is essential for improved patient outcomes and lower healthcare costs.
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