สืบค้นงานวิจัย
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Rajaraman S. - ไม่ระบุหน่วยงาน
ชื่อเรื่อง (EN): Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
ผู้แต่ง / หัวหน้าโครงการ (EN): Rajaraman S.
บทคัดย่อ (EN): Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer- aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trainedCNNbased DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose. © 2018 Rajaraman et al.
บทคัดย่อ: ไม่พบข้อมูลจากหน่วยงานต้นทาง
ภาษา (EN): en
เอกสารแนบ (EN): https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045518625&doi=10.7717%2fpeerj.4568&partnerID=40&md5=646329587f2133b4e617f62d2ff3a3ad
เผยแพร่โดย (EN): มหาวิทยาลัยมหิดล
คำสำคัญ (EN): software
เจ้าของลิขสิทธิ์ (EN): มหาวิทยาลัยมหิดล
หากไม่พบเอกสารฉบับเต็ม (Full Text) โปรดติดต่อหน่วยงานเจ้าของข้อมูล

การอ้างอิง

Rajaraman S.. "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images". ไม่ระบุหน่วยงาน. ม.ป.ป.

Rajaraman S.. "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images". ไม่ระบุหน่วยงาน. ม.ป.ป.. Print.



TARR Wordcloud:
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Rajaraman S.
มหาวิทยาลัยมหิดล
ไม่ระบุวันที่เผยแพร่
Effects of malaria parasite density on blood cell parameters Predictions of equilibrium solubility and mass transfer coefficient for CO2 absorption into aqueous solutions of 4-diethylamino-2-butanol using artificial neural networks Perceptions of asymptomatic malaria infection and their implications for malaria control and elimination in Laos Point-of-care lung ultrasound for the detection of pulmonary manifestations of malaria and sepsis: An observational study Association between subclinical malaria infection and inflammatory host response in a pre-elimination setting Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent Semantic segmentation of artery-venous retinal vessel using simple convolutional neural network In vitro study of parasite elimination and endothelial protection by curcumin: Adjunctive therapy for cerebral malaria Recent Progress in the Diagnosis of Pathogenic Candida Species in Blood Culture Draft genome of neurotropic nematode parasite Angiostrongylus cantonensis, causative agent of human eosinophilic meningitis
คัดลอก URL
กระทู้ของฉัน
ผลการสืบค้นทั้งหมด โพสต์     เรียงลำดับจาก
พัฒนาระบบโดยศูนย์ความรู้เฉพาะด้านวิศวกรรมความรู้และวิศวกรรมภาษาDeveloped by Center of Excellence for Unified Knowledge and Language Engineering
สนับสนุนโดยสำนักงานพัฒนาการวิจัยการเกษตร (องค์การมหาชน)Supported by the Agricultural Research Development Agency (Public Organization)
แบบสำรวจความพึงพอใจQuestionair