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In medical diagnostics, the invention of the computer-aided identification method has played a significant role in making essential decisions for human diseases. Lung cancer requires a greater focus among various diagnostic processes because both men and women are affected, contributing to high mortality rates. In addition, lung cancer is one of the leading causes of death worldwide. It can be treated if diagnosed at an early stage. Detecting and classifying lung lesions is challenging for radiologists. Radiologists typically use computer-aided diagnostic systems to screen for lung cancer. In recent years, computer specialists have proposed many techniques for diagnosing lung cancer. Conventional lung cancer prediction methods have failed to maintain the precision needed because the low-quality picture affects the segmentation process. Here, we propose a well-performing method to detect and classify lung cancer. We applied the Grey Wolf Optimization algorithm with a weighted filter to reduce noise in images, followed by segmentation using watershed transformation and dilation operations. In the end, we classified lung cancer among three classes using our method that showed high performance compared to previous studies: 98.33% accuracy, 100% sensitivity, and 93.33% specificity.

Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN