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Author Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero
Title Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. Type Conference Article
Year (down) 2018 Publication IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador Abbreviated Journal
Volume Issue Pages 1-6
Keywords
Abstract The identification of shrimp organs in biology using

histological images is a complex task. Shrimp histological images

poses a big challenge due to their texture and similarity among

classes. Image classification by using feature engineering and

convolutional neural networks (CNN) are suitable methods to

assist biologists when performing organ detection. This work

evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-

Words (PBOW) models for image classification leveraging big

data techniques; and transfer learning for the same classification

task by using a pre-trained CNN. A comparative analysis

of these two different techniques is performed, highlighting

the characteristics of both approaches on the shrimp organs

identification problem.
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Area Expedition Conference
Notes Approved no
Call Number gtsi @ user @ Serial 87
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