Lungs Nodule Detection using Semantic Segmentation and Classification with Optimal Features Talha Meraj 1 · Arslan Hassan 2 · Saliha Zahoor 3 · Hafiz Tayyab Rauf 4 · M.IkramUllah Lali 5 · liaqat Ali 6 · Syed Ahmad Chan Bukhari* 7 Abstract Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. Talha Meraj 1 Department of Computer Science, University of Gujrat, Gujrat, Pakistan. E-mail: talhameraj32@gmail.com Arslan Hassan 2 Department of Computer Science, University of Gujrat, Gujrat, Pakistan. E-mail: arslanwarraich223@gmail.com Saliha Zahoor 3 Department of Computer Science, University of Gujrat, Gujrat, Pakistan. E-mail: saliha.zahoor@uog.edu.pk Hafiz Tayyab Rauf 4 Department of Computer Science, University of Gujrat, Gujrat, Pakistan. E-mail: hafiztayyabrauf093@gmail.com M.IkramUllah Lali 5 Department of Computer Science, University of Gujrat, Gujrat, Pakistan. E-mail: ikramullah@uog.edu.pk liaqat Ali 6 School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China. E-mail: engr liaqat183@yahoo.com Syed Ahmad Chan Bukhari* 7 ( ) Division of Computer Science, Mathematics and Science (Healthcare Informatics), College of Professional Studies, St. John’s University, New York. Corresponding should be addressed to Syed Ahmad Chan Bukhari (bukharis@stjohns.edu) Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 14 September 2019 © 2019 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 14 September 2019 doi:10.20944/preprints201909.0139.v1 © 2019 by the author(s). Distributed under a Creative Commons CC BY license.