Abstract
The epidemic of malaria has caused many deaths for decades around the world. To shorten the morbidity of malaria, accurate and fast diagnostic tools are to be implied with Artificial Intelligence. In this research, an automated, fast, and accurate diagnostic model for one-shot detection and classification of malaria thin blood smears were developed using the Deep Siamese Capsule Network (D-SCN) and contributions for our research are threefold. Firstly, we proposed the D-SCN model which consists of two crucial phases which are feature extraction and feature discrimination. Secondly, we implied an end-to-end trained capsule network with an imperative routing (IR) mechanism for the feature extraction phase to capture feature invariances. Finally, at the feature discrimination phase,
Lorentz
,
L
1
and L
2
similarity metrics were proposed for the dissimilation of features. During experimentation, it is observed that the
Lorentz
similarity metric provided more discriminative capability by acquiring the least MSE at most of the instances. Further, an algorithm is proposed to obtain faster convergence by cautiously tuning the hyperparameters, and this aided in decreasing the noise scale in training the D-SCN. The experimental outcomes proved that D-SCN outperformed with the highest detection accuracy of 97.24% for Lorentz as a similarity measure, and the Capsule network with IR mechanism outperformed with the highest classification accuracy of 98.89%. To our knowledge, the proposed research implications are the first applications of D-SCN for one-shot detection and classification of thin blood smears with state-of-the-art performance.