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Related Experiment Video

Updated: Jun 29, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

A hybrid long short-term memory-convolutional neural network multi-stream deep learning model with Convolutional

Benjamin Appiah Yeboah1, Kojo Sam Micah1, Isaac Acquah1

  • 1Biomedical Engineering Program, Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

Science Progress
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning model combining LSTM, CNN, and CBAM effectively detects mpox. This AI tool shows high accuracy for early mpox diagnosis, aiding public health efforts.

Keywords:
Convolutional Block Attention MechanismLSTMMonkeypoxdeep learning modelmodel explainabilitymodel interpretabilitympox

Related Experiment Videos

Last Updated: Jun 29, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Infectious Disease Diagnostics

Background:

  • Mpox (monkeypox) is a zoonotic viral disease causing painful lesions, fever, and exhaustion.
  • Global outbreaks, including in non-endemic areas, necessitate improved early detection methods.
  • Deep learning shows promise for enhancing diagnostic capabilities for infectious diseases like mpox.

Purpose of the Study:

  • To develop a hybrid deep learning model for early mpox detection.
  • To integrate Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Block Attention Module (CBAM) for improved diagnostic accuracy.

Main Methods:

  • A multi-stream LSTM-CNN model with CBAM was developed and trained on the Mpox Skin Lesion Dataset v2.0.
  • LSTM and CNN layers were used for sequential and spatial feature extraction, respectively.
  • CBAM was employed for feature conditioning, with LIME and Grad-CAM for model interpretability.

Main Results:

  • The hybrid model achieved high performance with 94% accuracy, 94% F1-score, and 95.04% AUC.
  • The model demonstrated competitive results compared to existing state-of-the-art methods.
  • Interpretability techniques provided insights into the model's diagnostic reasoning.

Conclusions:

  • The developed LSTM-CNN-CBAM model is a reliable tool for early mpox detection.
  • The model's performance supports its potential integration into web and mobile platforms for accessible diagnosis.
  • This AI-driven approach offers a promising solution for managing mpox outbreaks.