EAI Endorsed Transactions on Pervasive Health and Technology Research Article 1 Real Time Lung Cancer Classification with YOLOv5 Shaif Mehraj Makhdoomi 1 , Cherry Khosla 2 , and Dr. Sagar Dhanraj Pande 3, * 1 Research Scholar, School of Computer Science & Engineering, Lovely Professional University, Phagwara, India. 2 Assistant Professor, School of Computer Science and Engineering, Lovely Professional University, Phagwara, India. 3 Assistant Professor Senior Grade, School of Computer Science & Engineering, VIT-AP University, Andhra Pradesh, India. Abstract Cancer must be appropriately categorized for effective diagnosis and treatment. Deep learning algorithms have shown tremendous promise in recent years for automating cancer classification. We used the deep learning system YOLOv5 to classify the four types of lung cancer in this study: big cell carcinoma, adenocarcinoma, normal lung tissue, and squamous cell carcinoma. We trained the YOLOv5 model using a publicly available database of lung cancer pictures. The dataset was divided into four categories: big cell carcinoma, adenocarcinoma, normal lung tissue, and squamous cell cancer. In addition, we compared YOLOv5's performance to older models such as SVM, RF, ANN, and CNN. The comparison found that YOLOv5 outperformed all these models, indicating its potential for the development of more accurate and efficient autonomous cancer classification systems. Conclusions from the research have important implications for cancer identification and therapy. Automatic cancer classification systems have the potential to increase the accuracy and efficacy of cancer detection, perhaps leading to better patient outcomes. The accuracy and speed of these systems can be enhanced by using deep learning techniques like YOLOv5, making them more effective for clinical applications. Our study's findings demonstrated high accuracy for every class, with a total accuracy of 97.77%. With the aid of accuracy, train loss, and test loss graphs, we assessed the model's performance. The graphs demonstrated how the model was able to gain knowledge from the data and increase its accuracy as it was being trained. The study's findings were also compiled in a table that gave a thorough assessment of each class's accuracy. Keywords: Adenocarcinoma, Artificial Intelligence, Automated diagnosis, Classification, Deep Learning, Image Analysis, Large Cell Carcinoma, Lung Cancer Classification, Machine Learning, Medical Imaging Received on 28 June 2023, accepted on 16 August 2023, published on 20 September 2023 Copyright © 2023 S. M. Makhdoomi et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY- NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited. doi: 10.4108/eetpht.9.3925 * Corresponding author. Email: sagarpande30@gmail.com 1. Introduction Cancer is a collection of diseases that are distinguished by uncontrollable cell proliferation and spread throughout the body. If left untreated, these cells can invade and destroy normal tissues and organs, causing serious health problems and even death. Lung cancer is both one of the most common and one of the worst types of cancer. It happens when abnormal cells in the lungs grow and reproduce uncontrollably, forming a tumor that can infiltrate nearby tissues and spread to other parts of the body. Lung cancer is usually fatal because it is detected late, after the cancer has spread beyond the lungs, making treatment more difficult. Cancer is a broad and complex term that refers to a collection of diseases that are caused by the uncontrolled growth and spread of abnormal cells in the body. Cancer cells are aberrant cells that could penetrate and destroy normal tissues and organs. It develops when genetic abnormalities arise within normal cells, causing the processes that control cell growth and division to malfunction. These mutations can be inherited or acquired during a person's lifespan because of a variety of circumstances such as carcinogen exposure (e.g., tobacco smoke, some chemicals, radiation), chronic inflammation, or certain infections. Because of its aggressive nature, late-stage identification, and high mortality rate, lung cancer is one of the leading EAI Endorsed Transactions on Pervasive Health and Technology 2023 | Volume 9