IRO Journals
Journal of Trends in Computer Science and Smart Technology

Hybrid Graph Mamba Framework for Histopathological Image Classification

Open Access
Published: 24 April, 2026
Pages: 304-323
  • Dave Machnaim , Dave Machnaim
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
  • Shawn Jaison , Shawn Jaison
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
  • Naveen Sundar , Naveen Sundar
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Narmadha Naveen D. , Narmadha Naveen D.
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
  • Sai Midhun Sai Midhun
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Karunya Institute of Technology and Sciences, Coimbatore, India
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How to Cite

Machnaim, Dave, Shawn Jaison, Naveen Sundar, Narmadha Naveen D., and Sai Midhun. 2026. “Hybrid Graph Mamba Framework for Histopathological Image Classification”. Journal of Trends in Computer Science and Smart Technology 8 (2): 304-23. https://doi.org/10.36548/jtcsst.2026.2.006.
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Keywords

Computational Pathology
Histopathological Image Analysis
Selective State-Space Models (Mamba)
Liquid Neural ODEs
Graph Neural Networks
Spectral Representation Learning
Functional Kernel Adaptive Networks (KAN)
Explainable-AI(XAI)

Abstract

Digital pathology has already been shown to be useful in disease diagnosis; however, the computational power required to use high-resolution digital pathology is substantial, making it unrealistic in many clinical laboratories due to limitations in hardware capabilities. Accordingly, this paper presents a new architecture, MFLA-Graph-Mamba-NCA, which addresses these limitations. The main point of MFLA-Graph-Mamba-NCA is to develop a lightweight model for tissue classification while maintaining speed and optimal accuracy. Four complementary learning paradigms are incorporated into the architecture:  a morphological frontend, a frequency-domain branch based on Functional Kernel Adaptive Networks (FunKAN), feature stabilization over time using Liquid Neural Ordinary Differential Equations, and global sequence modelling using a Graph-based Mamba module. The model was tested on a harmonized benchmark of 125,000 images, based on the NCT-CRC-HE-100K and LC25000 datasets, and divided into 12 different biological classes to expressly avoid cross-dataset lab leakage. It has been shown to provide 99.89%accuracy and a Macro: F1 score of 99.78%. These techniques were condensed to 1.46 million parameters, forming a hybridized model that can be deployed to standard clinical workstations.

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