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Lung Cancer Detection Using Deep Learning Techniques

Lung Cancer Detection Using Deep Learning Techniques

Manya Sangwan (J.C. Bose University of Science and Technology, India), Sapna Gambhir (J.C. Bose University of Science and Technology, India), and Sumita Gupta (Amity School of Engineering and Technology, Amity University, Noida, India)
Copyright: © 2023 |Pages: 26
DOI: 10.4018/978-1-6684-5255-4.ch009
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Abstract

Early detection that can be done to detect lung cancer is through radiological examination. Chest X-Ray or chest radiography is one of the tools that can be used to analyze lung diseases including pneumonia, bronchitis, and lung cancer. The image from the radiography will show the shape of the lungs difference between normal and abnormal lungs. In abnormal lungs, it will show nodules in the lungs on the results radiography image, but on the other hand, in normal lungs, it does not show nodules in the lungs on the radiographic image. This study to detect lung cancer using radiographic images using deep learning techniques. Therefore, by carrying out early revealing of lung cancer, it is hoped that this scheme will provide suitable action and directions for lung cancer patients and decrease lung cancer transience.
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Literature Review

Machine Learning in Medicine

Throughout the last decades, the field of medicine has been relying on the analysis of medical images (Litjens et al., 2017) from techniques such as radiographs (Shen et al., 2017), MRIs, ultrasound, or CT scans to detect, diagnose and find effective treatments for different diseases. Due to the enormous range of existing pathologies and the outsized number of factors that can lead humans to make a mistake in their diagnosis (Bruno et al., 2018), such as fatigue at work or a simple error in judgment, doctors and researchers are beginning to rely more and more on new technologies, capable of greatly facilitating their work. Throughout this document, we will focus on making the most of existing technologies to help specialists with tasks related to the analysis of medical images. To do this, we must introduce the main technologies and basic concepts that we will use.

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