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Liver Tumor Prediction using Attention-Guided Convolutional Neural Networks and Genomic Feature Analysis.

S Edwin Raja¹, J Sutha², P Elamparithi³

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.

|April 14, 2025

Summary

This study introduces Attention-Guided Convolutional Neural Networks (AG-CNNs) and Genomic Feature Analysis (GFAM) to improve liver tumor prediction by integrating imaging and genomic data for better diagnosis and prognosis.

Keywords:

Attention mechanism Attention-Guided Convolutional Neural Networks (AG-CNNs), Genomic Feature Analysis Module (GFAM)Liver tumor prediction Medical Imaging Multi-modal data fusion Radiomics Tumor segmentation Tumor subtype classification

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Area of Science:

Medical Image Analysis
Genomics
Oncology

Background:

Accurate liver tumor prediction is crucial for patient diagnosis and prognosis.
Challenges include understanding silent tumor characteristics and genomic-imaging feature interactions.

Purpose of the Study:

To develop integrated approaches for reliable liver tumor prediction.
To enhance segmentation accuracy and identify molecular markers for classification.

Main Methods:

Utilized Attention-Guided Convolutional Neural Networks (AG-CNNs) for accurate tumor segmentation from CT images.
Integrated a Genomic Feature Analysis Module (GFAM) for molecular marker identification.
Employed spatial and channel attention mechanisms within AG-CNNs for morphological profiling.

Main Results:

The proposed model achieved high accuracy (94.5%), Dice Similarity Coefficient (91.9%), and F1-Score (96.2%) on Dataset 3.
Outperformed existing methods in recall, precision, and specificity by up to 10% across different datasets.
AG-CNN demonstrated enhanced tumor region focus and segmentation accuracy.

Conclusions:

The integrated AG-CNN and GFAM approach significantly improves liver tumor prediction accuracy.
The methods provide a robust framework for subtype-specific tumor classification using genomic and imaging data.