Multimodal medical image fusion and classification using deep learning techniques

Author:

Veeraiah D.1,Sai Kumar S.2,Ganiya Rajendra Kumar3,Rao Katta Subba4,Nageswara Rao J.5,Manjith Ramaswamy6,Rajaram A.7

Affiliation:

1. Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna NTR Dt, Andhra Pradesh, India

2. Department of IT, PVP Siddhartha Institute of Technology, Andhra Pradesh, India

3. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P, India

4. Department of Computer Science & Engineering, B.V. Raju Institute of Technology, Narsapur, Medak (District), Telangana (State), India

5. Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, NTR Dt, Andhra Pradesh, India

6. Department of Electronics and Communication Engineering Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, Tamil Nadu, India

7. Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam, Tamil Nadu, India

Abstract

Medical image fusion plays a crucial role in accurate medical diagnostics by combining images from various modalities. To address this need, we propose an AI model for efficient medical image fusion using multiple modalities. Our approach utilizes a Siamese convolutional neural network to construct a weight map based on pixel movement information extracted from multimodality medical images. We leverage medical picture pyramids to incorporate multiscale techniques, enhancing reliability beyond human visual intuition. Additionally, we dynamically adjust the fusion mode based on local comparisons of deconstructed coefficients. Evaluation metrics including F1-score, recall, accuracy, and precision are computed to assess performance, yielding impressive results: an F1-score of 0.8551 and a mutual information (MI) value of 2.8059. Experimental results demonstrate the superiority of our method, achieving a remarkable 99.61% accuracy in targeted experiments. Moreover, the Structural Similarity Index (SSIM) of our approach is 0.8551. Compared to state-of-the-art approaches, our model excels in medical picture classification, providing accurate diagnosis through high-quality fused images. This research advances medical image fusion techniques, offering a robust solution for precise medical diagnostics across various modalities.

Publisher

IOS Press

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3