Multi-Model Detection of Lung Cancer Using Unsupervised Diffusion Classification Algorithm N. Jayanthi 1,* , D. Manohari 2 , Mohamed Yacin Sikkandar 3 , Mohamed Abdelkader Aboamer 3 , Mohamed Ibrahim Waly 3 and C. Bharatiraja 4 1 Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, Tamilnadu, India 2 Department of Computer Science and Engineering, St. Joseph’ s Institute of Technology, Chennai, 600119, Tamilnadu, India 3 Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia 4 Department of EEE, SRM Institute of Science and Technology, Chennai, 603203, India *Corresponding Author: N. Jayanthi. Email: jayanthi.nkpr@gmail.com Received: 28 March 2021; Accepted: 25 June 2021 Abstract: Lung cancer is a curable disease if detected early, and its mortality rate decreases with forwarding treatment measures. At first, an easy and accurate way to detect is by using image processing techniques on the cancer-affected images captured from the patients. This paper proposes a novel lung cancer detection method. Firstly, an adaptive median filter algorithm (AMF) is applied to prepro- cess those images for improving the quality of the affected area. Then, a super- vised image edge detection algorithm (SIED) is presented to segment those images. Then, feature extraction is employed to extract the mean, standard devia- tion, energy, contrast, etc., of the cancer-affected area. Finally, an unsupervised diffusion classification (UDC) algorithm is explored to narrow down the affected areas. The proposed lung cancer detection method is implemented on two datasets obtained from standard hospital real-time values. The experiment results achieved superior performance in the detection of lung cancer, which demonstrates that our new model can contribute to the early detection of lung cancer. Keywords: Diagnose lung cancer; future extraction; preprocessing; segmentation; UDC algorithm 1 Introduction Lung cancer is one of the most dangerous diseases afflicting people. It can typically be diagnosed using image processing techniques to quickly identify affected cancer areas, thereby decreasing decrease its development in time. Several factors may influence a rapid diagnosis of lung cancer, including tumor growth, late mortality due to uncertain efficacy, lack of specific screening, and rapid disease progression symptoms. The disease diagnosis depends, as well, on its performance dates. In the past few decades, the accuracy of these values has been declining, and consequently slowing down the pace of the fight against This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.018974 Article ech T Press Science