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Residual attention learning network and SVM for malaria parasite detection

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Abstract

Automated malaria parasite detection in thin-blood smear images is an important way to improve the diagnostic performance. Although deep convolutional neural networks (DCNNs) perform well in many image classification tasks, accurate classification of malaria parasite remains challenging due to the lack of training data, poor quality of smears and inter-class similarity. In this paper, we present a novel hybrid model dubbed RAL-CNN-SVM, which is composed of multiple residual attention learning convolution neural network (RAL-CNN) modules, a global average pooling (GAP) block and a classifier trained by a support vector machine (SVM). Each RAL-CNN block consists of residual learning and a new attention mechanism, which is mainly used to extract image depth activation features. The classification layer takes advantage of strong points of the SVM classification algorithm, solves the problem of nonlinear separable and improves the detection accuracy for malaria parasites. During our experiments, we evaluate the proposed RAL-CNN-SVM model on public Malaria Cell Images dataset, and the results show that the accuracy of the proposed novel hybrid model is 99.7%. We also visualized the class activation mapping (CAM) obtained by RAL-CNN50-SVM and ResNet50. The experimental results show that the RAL-CNN50-SVM (a single 50-layer model) model has strong attention ability and can highlight parasitic cells rather than background tissues in thin-blood smear images.

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Acknowledgements

We acknowledge the official NIH Website for the Public Malaria Cell Images dataset to support our research work. Meanwhile, we also acknowledge the National Natural Science Foundation of China (No.61370075) for funding our research work.

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  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Daiyi Li & Zongmin Ma

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Li, D., Ma, Z. Residual attention learning network and SVM for malaria parasite detection. Multimed Tools Appl 81, 10935–10960 (2022). https://doi.org/10.1007/s11042-022-12373-6

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