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A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

Asif Hasan Chowdhury, Md. Fahim Islam, M Ragib Anjum Riad, Faiyaz Bin Hashem, Md Tanzim Reza, Md. Golam Rabiul Alam

TL;DR

How the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information is discussed.

Abstract

The significant advancements in computational power cre- ate a vast opportunity for using Artificial Intelligence in different ap- plications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medi- cal specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available tech- nology offered by Tensorflow and Keras along with Microsoft-developed Vision Transformer, that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hy- brid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information.

A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

TL;DR

How the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information is discussed.

Abstract

The significant advancements in computational power cre- ate a vast opportunity for using Artificial Intelligence in different ap- plications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medi- cal specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available tech- nology offered by Tensorflow and Keras along with Microsoft-developed Vision Transformer, that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hy- brid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information.
Paper Structure (24 sections, 1 equation, 13 figures, 1 table)

This paper contains 24 sections, 1 equation, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. Research Objective
  3. Literature Review
  4. Methodology
  5. Dataset Collection Process
  6. Dataset Analysis and Interpretation
  7. Classification and Decision Classifier
  8. VGG-19
  9. Inception-v3
  10. DenseNet201
  11. SWIN Transformer
  12. Brief Work Steps
  13. Transfer and Transformer Fusion Model
  14. Federated Learning Centralized Server
  15. Implementation and Result Analysis
  16. ...and 9 more sections

Figures (13)

  • Figure 1: Top Level Over View of Proposed Disease Detection System
  • Figure 2: Sample Dataset of (a) COVID-19; (b) Pneumonia; (c) Normal
  • Figure 3: Transfer and Transformer Fusion Model
  • Figure 4: VGG-19 Training Accuracy and Validation Categorical Accuracy
  • Figure 5: DenseNet201 Training Accuracy and Validation Categorical Accuracy
  • ...and 8 more figures