Open Access

ARTICLE

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Walid El-Shafai^1,2, Noha A. El-Hag³, Ahmed Sedik⁴, Ghada Elbanby⁵, Fathi E. Abd El-Samie¹, Naglaa F. Soliman⁶, Hussah Nasser AlEisa^7,*, Mohammed E. Abdel Samea⁸

1 Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
2 Security Engineering Laboratory, Department of Computer Science, Prince Sultan University, Riyadh, 11586, Saudi Arabia
3 Higher Institute of Commercial Science, Management Information Systems, El-Mahala El-Kobra, Egypt
4 Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt
5 Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
6 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
7 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
8 Medical Imaging and Interventional Radiology, National Liver Institute, Menoufia University, Egypt

* Corresponding Author: Hussah Nasser AlEisa. Email:

Computers, Materials & Continua 2023, 74(2), 2905-2925. https://doi.org/10.32604/cmc.2023.031936

Received 30 April 2022; Accepted 12 July 2022; Issue published 31 October 2022

Abstract

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.

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Keywords

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