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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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A new multimodal medical image fusion framework using Convolution Neural Networks.

A Geetha Devi¹, Surya Prasada Rao Borra¹, P Rajesh Kumar²

¹Department of ECE, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India.

Journal of Medical Engineering & Technology

|April 11, 2025

Summary

This study introduces a deep learning framework for medical image fusion, optimizing convolutional layers and activation functions. The new method enhances diagnostic accuracy by creating superior composite images from multiple medical scans.

Keywords:

CT image Convolutional Neural Network Deep learning MRI image SPECT image activation function image fusion residual network

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Medical image fusion combines data from different imaging modalities to improve diagnostic information.
Current fusion methods can lose critical details, potentially impacting diagnostic accuracy.
Deep learning offers a promising approach to enhance medical image fusion.

Purpose of the Study:

To develop and evaluate a novel deep learning framework for medical image fusion.
To optimize the architecture, specifically the number of convolutional layers and activation function.
To improve the quality and detail preservation of fused medical images.

Main Methods:

A deep learning framework was designed with specific convolutional layer configurations and activation functions.
Element-wise fusion rules were employed to preserve minute image details.
The framework utilized three convolutional layers for feature extraction and three for image reconstruction.
Swish activation function was selected for intermediate layers.

Main Results:

The proposed framework with three convolutional layers and swish activation effectively extracted salient features.
Element-wise fusion rules prevented the loss of crucial details in the fused images.
Experimental results showed superior performance compared to conventional fusion methods across various metrics.
The quality of the fused images was significantly enhanced.

Conclusions:

The optimized deep learning framework provides a robust solution for medical image fusion.
The method demonstrates significant improvements in detail preservation and diagnostic quality.
This approach has the potential to enhance the efficiency and accuracy of medical diagnosis.