A Hybrid CNN-Tree Based Model for Enhanced Image Classification Performance

Blood cells play an essential role in various bodily functions, such as protection against infections and the body’s defense. The accurate classification of blood cells, generally grouped as red, white, and platelets is important for clinical diagnosis and hematological analysis. However, identifying these cells is a specialized and time-consuming process. Therefore, there is a hot-topic for high-precision automatic blood cell classification methods. Convolutional neural networks (CNNs) are a deep learning model used for visual data analysis and are very powerful in extracting features from data. In this study, we propose a hybrid classification model that combines the feature extraction power of CNNs with the ensemble-based prediction capabilities of Random Forest and XGBoost algorithms. The proposed hybrid model is compared with different methods on the BloodMNIST dataset in terms of classification performance and inference time. The results show that the tree-based methods outperform CNN by up to 8.49 and 11.62 points and achieve up to 82.9 times better inference times than other methods.