EAI Endorsed Transactions on Pervasive Health and Technology Review Article 1 Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review C. Usharani 1,* , B. Revathi 2 , A. Selvapandian 3 and S. K. Kezial Elizabeth 4 1,2 Ramco Institute of Technology, Rajapalayam, India 3 PSNA College of Engineering and Technology, Dindigul, India 4 Mangayarkarasi College of Engineering, Madurai, India Abstract INTRODUCTION: The Computed Tomography (CT) imaging-based Lung cancer detection is crucial for early diagnosis. This survey paper presents an overview of the techniques and advancements in CT-based lung cancer detection. It covers the fundamentals of CT imaging, including principles, types, and protocols. OBJECTIVES: The paper explores image processing techniques for pre-processing, such as noise reduction, enhancement, and segmentation. METHODS: Additionally, it discusses feature extraction methods, including shape, texture, and intensity-based features, as well as Deep Learning (DL) and Machine Learning (ML) methods for automated classification. RESULTS: Computerised systems and their integration is examined with CT imaging along with performance evaluation metrics. The survey concludes by addressing challenges, limitations, and future directions. The imaging modalities and artificial intelligence techniques are used to improve lung cancer detection. CONCLUSION: This comprehensive survey aims to provide a concise understanding of CT-based lung cancer detection for researchers and healthcare professionals. Keywords: Computed Tomography, Lung cancer, Machine Learning, Deep Learning, image processing Received on 04 December 2023, accepted on 23 February 2024, published on 01 March 2024 Copyright © 2024 C. Usharani 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.10.5265 * Corresponding author. Email: cusha91@gmail.com 1. Introduction One of the most prevalent and deadly forms of cancer worldwide is Lung cancer. It poses a significant public health challenge, accounting for substantial numerous deaths globally due to a cancer [1]. Early detection helps to provide a timely intervention and effective treatment strategies. The CT imaging has emerged as a vital tool in the lung cancer detection, offering detailed and cross-sectional images of the lungs with high resolution and accuracy [2, 3]. Over the years, advancements in CT technology and image analysis techniques have greatly improved the capabilities of lung cancer detection. CT imaging provides radiologists with the ability to visualize and characterize lung nodules, facilitating early diagnosis and subsequent treatment planning. The ability to precisely identify and classify these nodules is paramount in determining their malignancy and guiding appropriate clinical decisions. The process of lung cancer detection using CT imaging involves various stages, including image acquisition, pre-processing, classification and feature extraction. An Image pre-processing method used to minimise noise and enhance images, play a crucial role in improving the quality of CT images, thereby enhancing the detection of lung nodules [4]. Feature extraction methods extract quantitative measurements from the CT images, capturing relevant information about the shape, texture, and intensity characteristics of the nodules. These features serve as discriminative markers for distinguishing between benign and malignant nodules [5, 6]. Furthermore, ML and DL algorithms have shown remarkable potential in automating EAI Endorsed Transactions on Pervasive Health and Technology | Volume 10 | 2024 |