Complex or Irregular Lung Nodule Detection from Computed Tomography Scans

2026
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Authors: R. R. Sathiya; P. Sridhar; P. Kanishk Aadhaw; K. R. Laxman; B. Arvind; M. R. Dhanuprasad
Published in: Smart System for Integrated Computing and Communication
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the critical role of machine learning in detecting and classifying lung nodules from CT scans, emphasizing the importance of early cancer diagnosis. It explores various deep learning models, including VNet, and their effectiveness in reducing false positives and improving diagnostic accuracy. The text discusses the LUNA16 dataset and advanced preprocessing techniques, highlighting their significance in enhancing nodule detection. Additionally, it presents performance evaluation methods and training parameters, providing a comprehensive overview of the model's development and optimization. The chapter concludes with a discussion on the potential of AI integration in clinical practices, stressing the need for continuous improvements to address variations in nodule appearance and patient-specific factors.

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Title: Complex or Irregular Lung Nodule Detection from Computed Tomography Scans
Authors: R. R. Sathiya
P. Sridhar
P. Kanishk Aadhaw
K. R. Laxman
B. Arvind
M. R. Dhanuprasad
Publisher: Springer Nature Switzerland
Book: Smart System for Integrated Computing and Communication
Print ISBN: 978-3-031-99938-3

Electronic ISBN: 978-3-031-99939-0

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-031-99939-0_4

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