Performance Analysis Based on Deep Learning Architecture to Track Out Cholangiocarcinoma

DOI

10.2991/978-94-6239-664-7_2How to use a DOI?

Keywords

Cholangiocarcinoma Detection; Deep Learning; CNN; Transfer Learning; Disease Detection

Abstract

Deep learning has recently garnered significant attention for developing fast, automated, and accurate image classification and identification systems. This study focuses on enhancing and evaluating state-of-the-art deep convolutional neural network (CNN) architectures for imaging-based cholangiocarcinoma classification. The architectures analyzed include ResNet152V2, MobileNetV2, InceptionV3, Xception, VGG19, and DenseNet201. The experimental dataset comprised medical images of healthy and affected common bile duct tissue from 174 individuals, categorized into three groups. The primary objective was to establish a reliable and efficient model for early cholangiocarcinoma diagnosis using precise and rapid analytical methods. DenseNet201 demonstrated superior performance, exhibiting no signs of overfitting and achieving a test accuracy of 93%, outperforming other architectures in original and transfer learning approaches. Furthermore, DenseNet201 achieved this high performance with fewer parameters and reduced computational cost. The models were trained using the Keras framework with a Theano backend. This research provides valuable insights for data scientists and medical professionals seeking to improve diagnostic accuracy in cholangiocarcinoma detection.

Copyright

© 2026 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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TY  - CONF
AU  - Md Amzad Sadik Abid
AU  - Md. Tahmeed Kowsher Hameem
AU  - Md. Arafath Hossen Abir
AU  - Md. Moijeuddin Molla
AU  - Abdul Latif
AU  - Imrul Kayes Shefat
AU  - Abdul Kader
AU  - Ahnaf Tahmid Jamee
PY  - 2026
DA  - 2026/06/08
TI  - Performance Analysis Based on Deep Learning Architecture to Track Out Cholangiocarcinoma
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 7
EP  - 20
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_2
DO  - 10.2991/978-94-6239-664-7_2
ID  - Abid2026
ER  -