Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification

Author:

Aggarwal Meenakshi1,Khullar Vikas2ORCID,Goyal Nitin3ORCID,Rastogi Rashi4,Singh Aman567,Torres Vanessa Yelamos568,Albahar Marwan Ali9

Affiliation:

1. Bhagwan Parshuram Institute of Technology, Rohini New Delhi India

2. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

3. Department of Computer Science and Engineering, School of Engineering and Technology Central University of Haryana Mahendragarh India

4. Department of Computer Applications Chaudhary Charan Singh University Campus Meerut Uttar Pradesh India

5. Engineering Research & Innovation Group Universidad Europea del Atlántico Santander Spain

6. Islamic University Centre For Scientific Research The Islamic University Najaf Iraq

7. Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India

8. Department of Project Management Universidad Internacional Iberoamericana Campeche CP Mexico

9. Department of Computer Science Umm Al Qura University Mecca Saudi Arabia

Abstract

AbstractCorrect identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy‐preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open‐source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201‐based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor.

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

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