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M Nuthal Srinivasan1, Mohamed Yacin Sikkandar2, Maryam Alhashim3

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This study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN) for accurate Monkeypox detection. The ESACN model effectively classifies dermatological images, outperforming traditional methods for early disease diagnosis.

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AccuracyCapsule networksDeep learningImage classificationMulti-class classificationSpatial awareness

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dermatology

Background:

  • Accurate detection of Monkeypox virus (MPXV) is critical.
  • Traditional Machine Learning and Deep Learning models have limitations in classifying complex dermatological conditions.
  • Distinguishing visually similar skin conditions requires advanced image analysis techniques.

Purpose of the Study:

  • To introduce the Enhanced Spatial-Awareness Capsule Network (ESACN) for precise multi-class classification of dermatological images.
  • To address the shortcomings of existing models in differentiating conditions like monkeypox, chickenpox, and measles.
  • To leverage Capsule Networks' spatial hierarchy for improved diagnostic accuracy.

Main Methods:

  • Developed an Enhanced Spatial-Awareness Capsule Network (ESACN) architecture.
  • Utilized dynamic routing and spatial hierarchy inherent to Capsule Networks (CapsNets).
  • Applied the ESACN model to a dataset of 659 dermatological images across four classes: Monkeypox, Chickenpox, Measles, and Normal skin.

Main Results:

  • ESACN demonstrated superior performance in differentiating complex and visually similar skin conditions.
  • Significant improvements in accuracy, precision, recall, and F1 score were observed, even with limited data.
  • The model achieved robust and accurate classification for Monkeypox, Chickenpox, Measles, and Normal skin presentations.

Conclusions:

  • ESACN shows potential as a reliable tool for enhancing diagnostic accuracy in medical settings.
  • The model's ability to process spatial relationships aids in distinguishing dermatological conditions.
  • This approach can greatly aid in early diagnosis and treatment planning for skin diseases like Monkeypox.