Research Article Dense Convolutional Neural Network for Detection of Cancer from CT Images S. V. N. Sreenivasu, 1 S. Gomathi, 2 M. Jogendra Kumar, 3 Lavanya Prathap, 4 Abhishek Madduri, 5 Khalid M. A. Almutairi, 6 Wadi B. Alonazi, 7 D. Kali, 8 and S. Arockia Jayadhas 9 1 Department of Computer Science and Engineering, Narasaraopeta Engineering College, Narasaraopeta, Andhra Pradesh 522601, India 2 Department of Information Technology, Sri Sairam Engineering College, Chennai, Tamil Nadu 602109, India 3 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh 522502, India 4 Department of Anatomy, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu 600077, India 5 Department of Engineering Management, Duke University, North Carolina 27708, USA 6 Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box: 10219, Riyadh-11433, Saudi Arabia 7 Health Administration Department, College of Business Administration, King Saud University, PO Box: 71115, Riyadh-11587, Saudi Arabia 8 Department of Mechanical Engineering, Ryerson University, Canada 9 Department of EECE, St.Joseph university, Dar Es Salaam, Tanzania Correspondence should be addressed to S. Arockia Jayadhas; arockia.jayadhas@sjuit.ac.tz Received 8 March 2022; Revised 17 April 2022; Accepted 23 April 2022; Published 20 June 2022 Academic Editor: Yuvaraja Teekaraman Copyright © 2022 S. V. N. Sreenivasu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods. 1. Introduction When it comes to breast cancer screening, digital mammogra- phy (DM) is the gold standard for women who have no signs or symptoms of the disease. In a diagnostic setting, it has been demonstrated that DM can reduce breast cancer mortality. Rather than seeing clinical images as only graphical represen- tations, advances in medical image analysis have made to con- sider multidimensional data [1]. It is the process of analyzing medical images to extract information that is of interest to researchers that is referred to as radiomics. With the help of high-throughput computing approaches, it is possible to develop mathematical models and classifiers for diagnostic decision assistance [1], which analyze images and extract a Hindawi BioMed Research International Volume 2022, Article ID 1293548, 8 pages https://doi.org/10.1155/2022/1293548