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Advancements in Lung Cancer Prediction: An In-Depth Analysis and Optimization of Artificial Neural Network Algorithms

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
B. Pandu Ranga Raju
J. Hemalatha
S. Sai Keerthana
K. Madhavi
B. Himaja
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the advancements in lung cancer prediction using artificial neural network algorithms, focusing on VGG16, InceptionV3, and EfficientNetB0. The study provides a comprehensive overview of the current state of lung cancer incidence in India and the global significance of early detection. It explores the limitations of existing diagnostic techniques and the potential of machine learning algorithms to enhance prediction capabilities. The research thoroughly investigates these neural network methods, highlighting their performance in lung cancer prediction tasks and the impact of their findings on patient care and clinical practice. The study also discusses the development of a user-friendly interface for real-time detection, emphasizing its scalability, accessibility, and security features. The results demonstrate the effectiveness of these algorithms, with EfficientNetB0 achieving an impressive accuracy of 93%, underscoring their potential to revolutionize lung cancer diagnosis and treatment.
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previous chapter Performance Analysis of Electric Vehicle Battery Using Artificial Neural Networks
next chapter Diagnosis of Fetal Brain Abnormalities Using Ex Learning
Title
Advancements in Lung Cancer Prediction: An In-Depth Analysis and Optimization of Artificial Neural Network Algorithms
Authors
B. Pandu Ranga Raju
J. Hemalatha
S. Sai Keerthana
K. Madhavi
B. Himaja
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_11
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