An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases.
Journal:
Interdisciplinary sciences, computational life sciences
PMID:
32394271
Abstract
Malaria is one of the epidemics that can cause human death. Accurate and rapid diagnosis of malaria is important for treatment. Due to the limited number of data and human factors, the prediction performance and reliability of traditional classification methods are often affected. In this study, we propose an efficient and novel classification network named Attentive Dense Circular Net (ADCN) which based on Convolutional Neural Networks (CNN). The ADCN is inspired by the ideas of residual and dense networks and combines with the attention mechanism. We train and evaluate our proposed model on a publicly available red blood cells (RBC) dataset and compare ADCN with several well-established CNN models. Compared to other best performing CNN model in our experiments, ADCN shows superiority in all performance criteria such as accuracy (97.47% vs 94.61%), sensitivity (97.86% vs 95.20%) and specificity (97.07% vs 92.87%). Finally, we discuss the obtained results and analyze the difficulties of RBCs classification.