Hybrid Quantum-Classical Classification of Malaria Cell Images Using Transfer Learning

Abstract

Classifying malaria cell images relies on examining blood-smear microscopy slides to separate healthy red blood cells from those carrying the parasite to recognize the stage of infection. Manually examining the cells under a microscope is slow, as it depends heavily on expert skill, and can lead to inconsistent results, especially in resource-limited settings. Machine learning and deep learning help overcome these difficulties by automatically detecting subtle patterns that are hard for the human eye to distinguish, enabling faster, more accurate, and scalable diagnosis. This work compares the performance of classical deep learning models and hybrid quantum-classical systems for malaria cell classification problem, for a publicly available Kaggle dataset of 27,558 labelled images. The pipeline combines classical feature extraction from a subset of 5000 images with quantum feature mapping and variational quantum circuits (2, 3 and 4 qubits) through angle encoding and strongly entangling layers. All the models have been trained over 40 epochs with a 70-15-15 train-validation-test split, and accuracy, precision, recall, and F1-score have been evaluated. Classical CNN achieved 92.93% accuracy, while ResNet18 and MobileNetV2 achieved 87.87% and 60.67 % respectively.4-qubit Quantum CNN achieves the highest score of 93.47%, surpassing all the classical baselines. The hybrid MobileNetV2 achieved an accuracy of 87.07%, which is a significant improvement compared with its classical counterpart, indicating that quantum feature transformation can repurpose weak classical representations.