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Related Experiment Video

Updated: Jul 25, 2025

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

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Published on: August 16, 2020

A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.

Md Tarek Aziz¹, S M Hasan Mahmud^1,2, Md Fazla Elahe^1,3

¹Centre for Advanced Machine Learning and Applications (CAMLAs), Bashundhara R/A, Dhaka 1229, Bangladesh.

Diagnostics (Basel, Switzerland)

|June 28, 2023

Summary

This summary is machine-generated.

This study introduces a hybrid AI model for classifying osteosarcoma bone cancer subtypes from whole slide images. The model achieves high accuracy, aiding pathologists in diagnosing this common cancer in young adults.

Keywords:

MLP convolutional neural networks feature extraction feature selection machine learning osteosarcoma transfer learning

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

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Area of Science:

Computational pathology
Artificial intelligence in oncology
Digital histopathology

Background:

Osteosarcoma is the most prevalent bone cancer, primarily affecting adolescents and young adults.
Histopathology analysis of H&E-stained tissues presents challenges due to image complexity, noise, and class similarity.
Accurate classification of osteosarcoma subtypes (nontumor, necrosis, viable tumor) is crucial for effective treatment planning.

Purpose of the Study:

To develop and evaluate a hybrid deep learning framework for enhanced osteosarcoma tumor classification.
To improve the diagnostic accuracy and efficiency of classifying osteosarcoma subtypes from whole slide images (WSIs).
To integrate a robust feature selection mechanism for optimal model performance.

Main Methods:

A hybrid framework combining pre-trained Convolutional Neural Network (CNN) models with a Multilayer Perceptron (MLP) classifier was developed.
Transfer learning was employed using five CNN architectures as feature extractors on preprocessed WSIs.
Recursive Feature Elimination (RFE) with a decision tree estimator was utilized for feature selection, followed by MLP classification with five-fold cross-validation.

Main Results:

The proposed hybrid model achieved high accuracy: 95.2% for multiclass classification and 99.4% for binary classification.
Feature selection analysis identified optimal criteria balancing execution time and classification accuracy.
The model demonstrated superior performance compared to existing methods in osteosarcoma classification.

Conclusions:

The developed hybrid AI model significantly improves the accuracy of osteosarcoma tumor classification from digital histopathology slides.
This approach offers a valuable tool to assist clinicians in the diagnosis of osteosarcoma, potentially improving patient outcomes.
The model's integration into a web application enables real-time predictions, facilitating clinical application.