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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model.

Bahjat Fakieh1, Abdullah S Al-Malaise Al-Ghamdi1,2,3, Mahmoud Ragab3,4,5,6

  • 1Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Healthcare (Basel, Switzerland)
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PubMed
Summary
This summary is machine-generated.

This study introduces a novel Wind Driven Optimization with Deep Transfer Learning (WDODTL-ODC) method for accurate osteosarcoma detection and classification in biomedical images, improving early diagnosis and patient outcomes.

Keywords:
computer aided diagnosisdeep transfer learningimage processingmedical imagesosteosarcoma

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Osteosarcoma detection is challenging due to increasing cancer incidence and personalized treatments.
  • Manual osteosarcoma identification requires expertise and is time-consuming, impacting timely diagnosis.
  • Automated detection models offer a solution to reduce expert reliance and enable earlier osteosarcoma identification.

Purpose of the Study:

  • To develop an automated method for osteosarcoma detection and classification in biomedical images.
  • To enhance the accuracy and efficiency of osteosarcoma diagnosis through advanced computational techniques.
  • To introduce a novel Wind Driven Optimization with Deep Transfer Learning enabled Osteosarcoma Detection and Classification (WDODTL-ODC) method.

Main Methods:

  • Pre-processing using Gaussian filtering (GF) and contrast enhancement.
  • Feature extraction using deep transfer learning with the SqueezeNet model.
  • Classification employing the Wind Driven Optimization (WDO) algorithm and a deep-stacked sparse auto-encoder (DSSAE).

Main Results:

  • The WDODTL-ODC method demonstrated superior performance in osteosarcoma detection compared to existing models.
  • The integrated approach effectively identifies osteosarcoma in biomedical images.
  • The study validates the efficacy of combining deep transfer learning with optimization algorithms for medical image analysis.

Conclusions:

  • The WDODTL-ODC method provides an effective and efficient approach for osteosarcoma detection and classification.
  • Automated systems like WDODTL-ODC can significantly aid clinicians in diagnosing osteosarcoma.
  • Further research into AI-driven diagnostic tools holds promise for improving cancer care.