Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)

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Pancreatic Cancer Detection using deep learning for health care applications

Authors
Ahamed Rasool Madhina1, G. Rohini1, Glaret Subin2, J. Arunnehru3, P. Haja Syeddu Masooth4, *
1Department of Electrical and Electronics Engineering, S. A. Engineering College, Chennai, Tamilnadu, India
2Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu, India
3Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu, India
4Department of Mechanical Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamilnadu, India
*Corresponding author. Email: hajap@srmist.edu.in
Corresponding Author
P. Haja Syeddu Masooth
Available Online 30 June 2025.
DOI
10.2991/978-94-6463-754-0_44How to use a DOI?
Keywords
CNN; pancreatic cancer; deep learning; embedded systems; Adam and SDG ptimizer
Abstract

Convolutional Neural Network (CNN) deep learning model was designed and tailored to the specific cancer detection task, and the architecture is framed to accommodate the input data and the complexities of medical images. The integration of deep learning with embedded systems for healthcare applications is a novel and significant approach that can result in more effective and accessible diagnostic tools. The diverse and well-curated datasets containing pancreatic cancer medical images were collected, and the data were preprocessed for resizing, normalization, and augmentation to improve the model’s capacity for generalization. The preprocessed dataset was used to train the CNN model. The model is improved for efficiency by investigating model quantization, pruning, and other methods to decrease the model size without compromising performance since embedded systems have limited resources. By guaranteeing that the system satisfies latency requirements and retains accuracy, this facilitates rapid and on-the-spot decision-making, which is essential for healthcare applications. The suggested CNN model with Adam optimizer achieves the highest validation accuracy of 99.76%.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
Series
Atlantis Highlights in Engineering
Publication Date
30 June 2025
ISBN
978-94-6463-754-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-754-0_44How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ahamed Rasool Madhina
AU  - G. Rohini
AU  - Glaret Subin
AU  - J. Arunnehru
AU  - P. Haja Syeddu Masooth
PY  - 2025
DA  - 2025/06/30
TI  - Pancreatic Cancer Detection using deep learning for health care applications
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 501
EP  - 512
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_44
DO  - 10.2991/978-94-6463-754-0_44
ID  - Madhina2025
ER  -