Ahmed, A.H.Alwan, H.B.Çakmak, M.2024-09-292024-09-292023978-981197614-82367-3370https://doi.org/10.1007/978-981-19-7615-5_8https://hdl.handle.net/20.500.14619/9635International Conference on Data Analytics and Management, ICDAM 2022 -- 25 June 2022 through 26 June 2022 -- Jelenia Góra -- 292549Because of the great responsiveness of aspiratory knob location, computerized tomography (CT) is generally used to analyze cellular breakdown in the lungs without performing biopsy, which could make actual harm nerves and vessels. Notwithstanding, recognizing threatening and harmless aspiratory knobs stays troublesome. Since CT checks are regularly of low goal, it is challenging for radiologists to peruse the output picture’s subtleties. The proceeded with quick development of CT examine examination frameworks lately has made a squeezing need for cutting edge computational apparatuses to remove helpful highlights to help the radiologist in understanding advancement. PC-supported discovery (CAD) frameworks have been created to diminish notable mistakes by distinguishing the dubious highlights a radiologist searches for in a case survey. Our project aims to compare performance of various low memories, lightweight deep neural net (DNN) architectures for biomedical image analysis. It will involve networks like vanilla 2D CNN, U-Net, 2D SqueezeNet, and 2D MobileNet for two case classifications to discover the existence of lung cancer in patient CT scans of lungs with and without primary phase lung cancer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.eninfo:eu-repo/semantics/closedAccessAI modelCAD systemComputer tomographyPulmonary noduleConference Object10.1007/978-981-19-7615-5_82-s2.0-85152557579102Q489572