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Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.

Vishesh Tanwar¹, Bhisham Sharma¹, Dhirendra Prasad Yadav²

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|April 18, 2026

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This summary is machine-generated.

A new hybrid deep learning model significantly improves early pancreatic cancer (PC) detection using CT images. This AI framework achieves high accuracy with fewer resources, aiding clinical diagnosis.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Early and accurate detection of pancreatic cancer (PC) is a critical clinical challenge.
Current diagnostic methods for PC face limitations in speed and accuracy.

Purpose of the Study:

To develop a novel, computationally efficient hybrid deep learning framework for automated pancreatic cancer classification from CT images.
To enhance diagnostic performance in PC detection.

Main Methods:

A hybrid deep learning framework integrating MobileNetV3Small, a convolutional block attention module, and Low-rank Attention with Shared Efficient Representations (LASER).
Transformer encoder for capturing long-range dependencies and a cross-type interaction (CTI) module for feature fusion.
Automated classification of CT images.

Keywords:

attention classification deep learning pancreatic cancer transformer

Related Experiment Videos

Main Results:

Achieved 99.34% accuracy, 0.9996 AUC-ROC, 0.9897 Cohen's Kappa, and 0.9859 MCC on 18,942 CT images.
Outperformed ResNet50, EfficientNetB0, and ViT variants with only 1.26 million parameters.
Demonstrated high diagnostic performance with reduced computational requirements.

Conclusions:

Explainability analyses (Grad-CAM, Grad-CAM++, attention visualization) confirm the model focuses on clinically relevant regions.
The proposed framework offers a promising approach for accurate and efficient pancreatic cancer detection.