Indonesian Journal of Electrical Engineering and Computer Science Vol. 28, No. 2, November 2022, pp. 987~993 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v28.i2.pp987-993  987 Journal homepage: http://ijeecs.iaescore.com Lung cancer detection using image processing and deep learning Asraa A. Abd Al-Ameer 1 , Ghufran Abdulameer Hussien 2 , Hajer. A. Al Ameri 1 1 Department of Mathematics, Faculty of Education, Al-Zahraa University for Women, Karbala, Iraq 2 Department of Business Administration, Al-Mustaqbal University College, Babel, Iraq Article Info ABSTRACT Article history: Received Apr 21, 2022 Revised Aug 12, 2022 Accepted Aug 31, 2022 This project is about the detection of lung cancer by training a model of deep neural networks using histopathological lung cancer tissue images. Deferent models have been proposed for detecting lung cancer cells automatically involving Inception V3, Random Forest, and convolutional neural network (CNN). The deep convolutional neural network has been trained to extract important features that facilitate build detection and diagnosis of lung cancer cells more efficiently and accurately. The proposed method in this project has accomplished promising and satisfactory results in terms of accuracy, precision, recall, F-score, and specificity measure in lung cancer detection. Furthermore, it has been applied on dataset which contains 178,000 photos. The accuracy values that are obtained are accuracy 97.09%, precision 96.89%, recall 97.31%, F-score measure 97.09%, and specificity measure 96.88%. Keywords: Classification Deep learning Histopathology Image compression Image processing Lung cancer detection This is an open access article under the CC BY-SA license. Corresponding Author: Asraa A. Abd Al-Ameer Department of Mathematics, Faculty of Education, Al-Zahraa University for Women Karbala, Iraq Email: asraa.abd.alhussien@alzahraa.edu.iq 1. INTRODUCTION Cancer is the most common malignancy worldwide, caused primarily by smoking and exposure to substances such as radon, arsenic, and asbestos. Furthermore, the use of preservative-laced manufactured foods [1]. Lung cancer is cancer that starts in the lungs and spreads throughout the body. The lungs, which receive oxygen through inhalation and release carbon dioxide through exhalation, are too spongy in the chest [2]. It is the main cause of cancer death in both men and women in the United States and many other countries [3], [4]. Every year, about 150,000 people die from lung cancer, while another 200,000 people are diagnosed with the condition [5]. Like all cancers, cancer of the lung is resulting from an abnormality in the body's basic unit of life [6]. Manually, lung cancer is detected by observing lung tissue images, but the manual process of detection is time consuming [7], [8]. The better approach is to build a model which can detect whether a person is having cancer or not in a few minutes [9]. Such a model can be built with the help of image processing and neural networks. So, the aims and the objectives of the proposed work are to construct a program used for the detection of lung cancer using python machine learning, so it could be able to: i) Decrease the rules for testing, ii) To reduce the time and cost required for various excessive medical tests, iii) For improving the manual analysis, iv) Early-stage detection of cancer, and v) Increasing the survivability of the patient. Image processing explained in [10] has been used with the abilities of deep learning neural networks explained in [11] to perform this task semi-automatically by the computer quickly and efficiently. The findings imply that deep learning models can provide fast, accurate, and low-cost cancer detection to both professionals and patients, and hence have a substantial impact on cancer detection.