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Related Concept Videos

Classification of Bones

Classification of Bones

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.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

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Related Experiment Video

Updated: Jul 30, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach.

Ioannis A Vezakis¹, George I Lambrou^1,2,3, George K Matsopoulos¹

¹Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou St., 15780 Athens, Greece.

|May 16, 2023

Summary

The smallest deep learning network and image size achieved 91% accuracy in classifying osteosarcoma (bone cancer) histopathology. Larger networks did not improve performance, highlighting the need for efficient model selection in cancer diagnosis.

Keywords:

deep learning machine learning neural networks osteosarcoma

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Area of Science:

Oncology
Biomedical Engineering
Computational Pathology

Background:

Osteosarcoma is the most common primary bone cancer, particularly affecting children and adolescents.
Histopathology is crucial for staging and treatment decisions in osteosarcoma.
Machine learning and deep learning show promise for analyzing histopathological images.

Purpose of the Study:

To compare the performance of deep neural networks for osteosarcoma histopathological evaluation.
To identify optimal deep learning models and configurations for accurate osteosarcoma classification.

Main Methods:

Utilized publicly available osteosarcoma cross-section images.
Analyzed and compared state-of-the-art deep neural networks.
Employed 5-fold cross-validation for model training and evaluation.

Main Results:

Classification performance did not consistently improve with larger neural networks.
The smallest network (MobileNetV2) with the smallest input image size yielded the best results.
MobileNetV2 achieved 91% overall accuracy in osteosarcoma classification.

Conclusions:

Careful selection of network architecture and input image size is critical.
Smaller, efficient networks can outperform larger, more complex models.
Optimizing network selection can enhance osteosarcoma diagnostic accuracy and patient outcomes.