Embedded System-Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model

The disease of malaria, transmitted by female Anopheles mosquitoes, is highly contagious, resulting in numerous deaths across various regions. Microscopic examination of blood cells remains one of the most accurate methods for malaria diagnosis, but it is time-consuming and can produce inaccurate results occasionally. Due to machine learning and deep learning advances in medical diagnosis, improved diagnostic accuracy can now be achieved while costs can be reduced compared to conventional microscopy methods. This work utilizes an open-source dataset with 26 161 blood smear images in RGB for malaria detection. Our preprocessing resized the original dimensions of the images into 64 x 64 due to the limitations in computational complexity in developing embedded systems-based malaria detection. We present a novel embedded system approach using 119 154 trainable parameters in a lightweight 17-layer SqueezeNet model for the automatic detection of malaria. Incredibly, the model is only 1.72 MB in size. An evaluation of the model's performance on the original NIH malaria dataset shows that it has exceptional accuracy, precision, recall, and F1 scores of 96.37%, 95.67%, 97.21%, and 96.44%, respectively. Based on a modified dataset, the results improved further to 99.71% across all metrics. Compared to current deep learning models, our model significantly outperforms them for malaria detection, making it ideal for embedded systems. This model has also been rigorously tested on the Jetson Nano B01 edge device, demonstrating a rapid single image prediction time of only 0.24 s. The fusion of deep learning with embedded systems makes this research a crucial step toward improving malaria diagnosis. In resource-constrained settings, the model's lightweight architecture and accuracy enhancements hold great promise for addressing the critical challenge of malaria detection.