Advancing Malaria Identification From Microscopic Blood Smears Using Hybrid Deep Learning Frameworks

Malaria is a mosquito-borne, life-threatening, and contagious disease that has caused thousands of fatalities in recent years. Due to inadequate detection, the inexperience of laboratory personnel, and lack of advanced point-of-care equipment, the malaria-induced mortality rate is increasing. In addition to the traditional detection mechanisms, researchers have recently been investigating microscopic malaria-infected Red Blood Cells (RBC) image analysis based on deep learning models to detect malaria parasites as a general-purpose point-of-care solution. In this paper, we develop several hybrid data-driven models by combining a convolutional neural network (CNN) to extract the relevant features and two cascaded recurrent neural networks (RNNs) classifiers. Gated recurrent unit (GRU), long short-term memory (LSTM), and Bi-directional LSTM (BiLSTM) are considered candidate RNN classifiers. The models are compared in terms of accuracy, type-I & II error rates, and inference & training computation time. Experimental results demonstrate that the CNN-LSTM-BiLSTM model outperforms the other models with a significantly higher accuracy (96.20%), less type-I error rate (2.23%), and fewer combined type-I and type-II errors (3.80%). Also, we consider the model computation time (both training time per epoch and inference time per step)‘ as an important metric for emerging distributed learning paradigms where point-of-care devices with IoT (Internet of Things) capability can jointly contribute to global model accuracy. Thus, our findings demonstrate the practicality of cascading classifiers in resource-constrained point-of-care devices.