Hybrid deep and machine learning-based classification of malaria-infected blood cells using texture, morphological, and statistical features

Malaria remains one of the most dangerous infectious diseases in tropical and sub-Saharan regions, requiring efficient, accurate, and interpretable diagnostic systems. This paper presents a hybrid system that combines machine learning and deep learning techniques to classify malaria-infected blood cells from microscopic images. The approach integrates artificial features such as texture features from the Gray Level Co-occurrence Matrix (GLCM), morphological features, and statistical features with deep features extracted from pre-trained convolutional neural networks like ResNet50 and VGG16. After preprocessing steps, including gamma correction, HSV color space transformation, and contrast-limited adaptive histogram equalization (CLAHE), the cells were segmented using local entropy thresholding and filtered to remove noise smaller than 125 pixels. The combined handcrafted and deep features were classified using Support Vector Machine (SVM), Random Forest (RF), and XGBoost classifiers, evaluated through 10-fold cross-validation. Results demonstrate that the hybrid model significantly improves performance over methods based on single features. The XGBoost classifier achieved the highest accuracy at 95.4%, with precision of 95.1%, recall of 94.8%, and an AUC of 0.99.