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Related Experiment Video

Updated: Jun 5, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on

Yousra Hadhoud¹, Tahar Mekhaznia¹, Akram Bennour¹

¹LAMIS Laboratory, Larbi Tebessi University, Tebessa 12002, Algeria.

Diagnostics (Basel, Switzerland)

|December 17, 2024

Summary

This summary is machine-generated.

A new hybrid model combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) accurately detects Tuberculosis and distinguishes between Pneumonia types from chest X-rays. This Computer-Aided Diagnosis (CAD) system shows high accuracy, aiding in resource-limited settings.

Keywords:

X-rays chest diseases classification convolutional neural networks vision transformers

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Artificial Intelligence in Medical Imaging
Deep Learning for Diagnostic Systems
Radiographic Image Analysis

Background:

Chest disease identification, particularly for Tuberculosis and Pneumonia, faces diagnostic challenges due to overlapping radiographic features.
Limited availability of expert radiologists exacerbates diagnostic difficulties, especially in developing countries.
A need exists for objective and consistent analysis of chest X-ray images to reduce human error in diagnosis.

Purpose of the Study:

To develop a Computer-Aided Diagnosis (CAD) system for analyzing chest X-ray images.
To accurately detect Tuberculosis and differentiate between Tuberculosis and Pneumonia using a hybrid AI model.
To leverage the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for enhanced diagnostic performance.

Main Methods:

A two-step hybrid model integrating ResNet-50 CNN with the ViT-b16 architecture was designed.
Transfer learning was employed using datasets from Guangzhou Women's and Children's Medical Center (Pneumonia) and Qatar/Dhaka universities (Tuberculosis).
The model combines CNNs' hierarchical feature extraction with ViTs' self-attention mechanisms for improved classification.

Main Results:

The hybrid CNN-ViT model achieved 98.97% accuracy in binary classification for Tuberculosis detection.
For multi-class classification (Tuberculosis, viral Pneumonia, bacterial Pneumonia), the model reached 96.18% accuracy.
These results indicate significant potential for improving diagnostic accuracy and reliability in chest disease classification.

Conclusions:

The proposed hybrid CNN-ViT model shows substantial potential for advancing CAD systems in chest disease diagnosis.
Integrating CNN and ViT architectures enhances diagnostic precision, offering a robust solution for complex radiographic analyses.
This approach can alleviate healthcare burdens in resource-limited settings and improve patient outcomes for chest diseases.