Abstract

Echinococcosis—a parasitic disease caused by Echinococcus granulosus or Echinococcus multilocularis larvae—occurs in many regions in the world. This disease can pose a serious threat to public health and thus requires a convenient and cost-effective method for early detection. So, we developed a novel method based on visual saliency and scale-invariant features that detects the tapeworm parasites. This method improves upon existing bottom-up computational saliency models by introducing a visual attention mechanism. The results indicated that the proposed method offers a higher level of both accuracy and computational efficiency when detecting Echinococcus granulosus protoscoleces, which in turn could improve early detection of echinococcosis.

© 2019 Chinese Laser Press

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