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Sickle cell disease classification using deep learning.

Sanjeda Sara Jennifer¹, Mahbub Hasan Shamim¹, Ahmed Wasif Reza¹

¹Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh.

|December 4, 2023

Summary

This summary is machine-generated.

This study explores deep learning for Sickle Cell Disease (SCD) classification using image augmentation. MobileNet showed significant improvement, while ResNet-50 achieved perfect scores on specific cell shapes.

Keywords:

Ablation experiment Classification Deep learning model Machine learning classifier Sickle cell disease

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Area of Science:

Medical Imaging
Computational Biology
Machine Learning

Background:

Sickle Cell Disease (SCD) diagnosis relies on accurate cell morphology analysis.
Deep learning offers potential for automated and precise SCD classification.

Purpose of the Study:

To evaluate transfer and deep learning models for SCD classification.
To enhance model robustness using image augmentation and adversarial testing.

Main Methods:

Implemented ResNet-50, AlexNet, MobileNet, VGG-16, VGG-19, and CNN models.
Utilized the ErythrocytesIDB dataset with advanced image augmentation.
Conducted ablation studies with Random Forest and SVM, including hyperparameter tuning.

Main Results:

ResNet-50 achieved 100% precision, recall, and F1-score for specific cell shapes.
AlexNet demonstrated high precision (98%) and recall (99%) for circular and elongated cells.
MobileNet showed statistically significant improvement (p=0.0229) in SCD classification.

Conclusions:

Transfer and deep learning models show promise for SCD classification.
Image augmentation is crucial for dataset robustness and model accuracy.
MobileNet and ResNet-50 exhibit strong performance, with MobileNet showing significant statistical improvement.