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Related Experiment Video

Updated: Sep 9, 2025

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

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

Published on: August 16, 2020

Ocotillo optimization-driven deep learning for bone marrow cytology classification.

Doaa Sami Khafaga¹, El-Sayed M El-Kenawy^2,3, Faris H Rizk⁴

¹Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

|August 29, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning framework with the Ocotillo Optimization Algorithm (OcOA) to improve bone marrow cell classification for hematological cancers. The OcOA-enhanced model achieved 98.24% accuracy, significantly boosting diagnostic reliability.

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Last Updated: Sep 9, 2025

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

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Published on: August 16, 2020

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Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Area of Science:

Medical Diagnostics
Computational Biology
Artificial Intelligence

Background:

Manual diagnosis of hematological cancers via bone marrow smear analysis is labor-intensive and error-prone.
Expert knowledge dependency limits current diagnostic methods for leukemia and other blood cancers.

Purpose of the Study:

To develop an advanced deep learning framework for accurate and efficient bone marrow cell classification.
To enhance diagnostic reliability in hematological screening using bio-inspired optimization algorithms.

Main Methods:

Developed a baseline Convolutional Neural Network (CNN) for cell classification.
Integrated the Ocotillo Optimization Algorithm (OcOA) for feature selection (bOcOA) and hyperparameter optimization (continuous OcOA).

Main Results:

Baseline CNN achieved 86.29% accuracy.
OcOA integration improved CNN accuracy to 98.24%, with sensitivity reaching 98.34% and specificity 98.14%.
Reduced average classification error significantly, enhancing diagnostic reliability.

Conclusions:

Metaheuristic optimization techniques, like OcOA, can significantly improve deep learning model effectiveness in clinical diagnostics.
The proposed approach offers a scalable, interpretable, and accurate solution for automated cytology and hematological screening.