In WCE images has not been fully explored.Īutomatic hookworm detection in WCE images remainsĪ challenging task. Unfortunately, automatic hookworm detection, It is reported that over one million patients globally have been examined with WCE, which has been widely used for several inflammatory bowel diseases and disorders in recent years, such as bleeding, polyp, ulcer, tumor, Crohn's disease, and Lesion detection solutions have been proposed recently.
HOOKWORMS IN HUMANS IMAGES SERIES
To assist the endoscopists, a series of automatic Which usually take a couple of hours to manually examine To identify suspicious areas and analyze the potential diseases, It is a laborious and tedious process for trained endoscopists To capture the whole GI tract, totally around 50 000 images. Images of GI tract per second, which will last for a few hours The digestive system to collect images or physiological data after swallowed by the patient. Wireless Capsule Endoscopy (WCE) travels through It is reported that hookworm has affected more than 600 million people worldwide.Īs a miniature medical device for gastrointestinal (GI) diagnosis, Hookworm infection seriously threatens human health, which will impair the physical and intellectual development of children. It is a leading cause of maternal and child morbidity in developing countries of the tropics and subtropics due to poor sanitation.
HOOKWORM is an infection by a parasitic bloodsucking roundworm. Sensitivity and specificity makes the proposed method That the proposed approach outperforms the state-of-the artĪpproaches. WCE datasets with 440K images, which demonstrate Hookworm classification network, to enhance the featureĤ)Experiments have been conducted on one of the largest Proposed to integrate the tubular regions obtained fromĮdge extraction network and the feature maps from
These two networks are combined to avoid theĮdge feature caching and speed up the classification.ģ)More specifically, two novel edge pooling layers are Model visual appearances and tubular patterns of hookworms. Integrates two CNN networks, edge extraction networkĪnd hookworm classification network, to simultaneously Learning framework specifically designed for hookwormĢ)The proposed hookworm detection framework seamlessly The main contributions of this project are summarized as follows:ġ) To the best of our knowledge, this is the first deep The quality of WCE images is usually poor due to the hardware limitation and the light condition. It is a leading cause of maternal and child morbidity in developing countries of the tropics and subtropics due to poor sanitation.Īutomatic hookworm detection in WCE images remains a challenging task.
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