Citation

AlDahoul, Nouar and Abdul Karim, Hezerul and Kee, Shaira Limson and Tan, Myles Joshua Toledo (2022) Localization and Classification of Parasitic Eggs in Microscpic Images Using An Efficientdet Detector. In: 2022 IEEE International Conference on Image Processing (ICIP), 16-19 Oct 2022, Bordeaux, France.

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Abstract

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Image processing, EfficientDet , microscopic image , multi-modal learning , object detection , parasitic egg
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	14 Mar 2023 03:31
Last Modified:	14 Mar 2023 03:31
URII:	http://shdl.mmu.edu.my/id/eprint/11226

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