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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with

Elsaid Md Abdelrahim1,2, Hasan Hashim3, El-Sayed Atlam2,3

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Summary

This study developed an ensemble model for monkeypox detection, achieving 95.45% accuracy in classifying skin lesions. The model aids in differentiating monkeypox from similar diseases, supporting public health efforts.

Keywords:
deep learningimbalanced datamachine learningmonkeypoxoptimizationprediction

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Area of Science:

  • Medical Informatics
  • Computer Vision
  • Epidemiology

Background:

  • Monkeypox (MPX) outbreaks outside endemic regions necessitate rapid diagnostic tools.
  • Differentiating MPX from similar dermatological conditions like chickenpox and measles is clinically challenging.
  • The Monkeypox Skin Lesion Dataset (MSLD) was curated for this study, containing 770 images from 162 patients across four classes: MPX, measles, chickenpox, and normal.

Purpose of the Study:

  • To develop and evaluate an ensemble machine learning model for accurate monkeypox skin lesion classification.
  • To address the challenge of early clinical differentiation between monkeypox and other exanthematous diseases.
  • To create a computational tool for enhanced disease monitoring and outbreak management.

Main Methods:

  • An ensemble model was created by integrating transformer models and a Support Vector Machine (SVM) classifier.
  • Seven Convolutional Neural Network (CNN) architectures were evaluated for feature extraction.
  • The top four performing CNNs (EfficientNetB0, ResNet50, MobileNet, Xception) were selected for feature extraction, with subsequent concatenation and optimization before SVM classification.

Main Results:

  • The proposed ensemble model achieved a high diagnostic accuracy of 95.45% for monkeypox detection.
  • The model demonstrated excellent performance metrics, including precision (95.51%), recall (95.45%), and F1 score (95.46%).
  • These results underscore the model's effectiveness in accurately identifying monkeypox lesions within the dataset.

Conclusions:

  • The hybrid ensemble framework exhibits robust diagnostic performance for monkeypox detection.
  • The model's high accuracy and computational efficiency suggest its potential as a valuable clinical decision support tool.
  • This approach can contribute to improved disease surveillance and management strategies during outbreaks.