Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application
Authors
Keywords
-
Journal
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Volume 2020, Issue -, Pages 1-15
Publisher
Hindawi Limited
Online
2020-07-09
DOI
10.1155/2020/8895429
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Multi-Aspect Aware Session-Based Recommendation for Intelligent Transportation Services
- (2020) Yin Zhang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Deep learning approach to detect malaria from microscopic images
- (2019) Vijayalakshmi A et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization
- (2019) M. Shamim Hossain et al. ACM Transactions on Multimedia Computing Communications and Applications
- Edge Intelligence in the Cognitive Internet of Things: Improving Sensitivity and Interactivity
- (2019) Yin Zhang et al. IEEE NETWORK
- Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics
- (2019) Arun Kumar Sangaiah et al. IEEE Transactions on Industrial Informatics
- COCME: Content-Oriented Caching on the Mobile Edge for Wireless Communications
- (2019) Yin Zhang et al. IEEE WIRELESS COMMUNICATIONS
- Heterogeneous Information Network-Based Content Caching in the Internet of Vehicles
- (2019) Yin Zhang et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- A name disambiguation module for intelligent robotic consultant in industrial internet of things
- (2019) Xiao Ma et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- Image analysis and machine learning for detecting malaria
- (2018) Mahdieh Poostchi et al. Translational Research
- Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
- (2018) Sivaramakrishnan Rajaraman et al. PeerJ
- Emotion-Aware Multimedia Systems Security
- (2018) Yin Zhang et al. IEEE TRANSACTIONS ON MULTIMEDIA
- Automatic Fruit Classification Using Deep Learning for Industrial Applications
- (2018) M. Shamim Hossain et al. IEEE Transactions on Industrial Informatics
- Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner
- (2017) Gopalakrishna Pillai Gopakumar et al. Journal of Biophotonics
- A software defined network routing in wireless multihop network
- (2017) Junfeng Wang et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks
- (2017) Dhanya Bibin et al. IEEE Access
- Deep Relative Attributes
- (2016) Xiaoshan Yang et al. IEEE TRANSACTIONS ON MULTIMEDIA
- The utility of serial blood film testing for the diagnosis of malaria
- (2015) Karan S. Makhija et al. PATHOLOGY
- Towards context-sensitive collaborative media recommender system
- (2014) Mohammed F. Alhamid et al. MULTIMEDIA TOOLS AND APPLICATIONS
- The Schisto Track: A System for Gathering and Monitoring Epidemiological Surveys by Connecting Geographical Information Systems in Real Time
- (2014) Onicio B Leal Neto et al. JMIR mHealth and uHealth
- An automatic device for detection and classification of malaria parasite species in thick blood film
- (2012) Saowaluck Kaewkamnerd et al. BMC BIOINFORMATICS
- Data Interoperability and Multimedia Content Management in e-Health Systems
- (2012) M. Masud et al. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
- Machine learning approach for automated screening of malaria parasite using light microscopic images
- (2012) Dev Kumar Das et al. MICRON
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now