ColoSeqNet: A Hybrid Deep Learning Model for Enhanced Detection of Colorectal Cancer

DOI

10.2991/978-94-6463-978-0_39How to use a DOI?

Keywords

Colorectal Cancer Detection; Hybrid Deep Learning; Enhanced ResNet50; LSTM; Histopathology Image Classification

Abstract

In the meantime, the standard approaches predominantly focus on extracting spatial features from pre-trained convolutional neural network (CNN) architectures, including VGG16, InceptionV3, and DenseNet121, while rarely considering the sequential dependencies intrinsic to histopathological patterns. This constraint hinders their ability to represent intricate dependencies among image attributes, leading to inaccurate classification performance. This paper proposes a new hybrid deep learning framework (ColoSeqNet) for colorectal cancer detection to tackle these challenges. The proposed approach leveraged an enhanced ResNet 50 model integrated with long short-term memory (LSTM) layers for powerful spatial feature extraction and sequential learning ability. We use the improved ResNet50 for fine-tuning to fit a model on the colorectal histopathology images and LSTM layers for preserving sequential correlations and dependencies among extracted features. The ColoSeqNet architecture is validated on the open-to-access Colorectal Cancer Histology dataset. Results show that our model achieves an outstanding accuracy of 96.89% compared to traditional pre-trained models and recent state-of-the-art approaches.

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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TY  - CONF
AU  - Boddupelli Durgabhavani
AU  - Amjan Shaik
PY  - 2025
DA  - 2025/12/31
TI  - ColoSeqNet: A Hybrid Deep Learning Model for Enhanced Detection of Colorectal Cancer
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 449
EP  - 461
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_39
DO  - 10.2991/978-94-6463-978-0_39
ID  - Durgabhavani2025
ER  -