research-article

Deep and Confident Image Analysis for Disease Detection

Authors:

Stephen Wambura,

He LiAuthors Info & Claims

VSIP '20: Proceedings of the 2020 2nd International Conference on Video, Signal and Image Processing

Pages 91 - 99

https://doi.org/10.1145/3442705.3442720

Published: 21 March 2021 Publication History

Abstract

This paper proposes an efficient deep learning classifier built on Bayesian deep neural network framework for general probabilistic disease detection along with reliable principled uncertainty estimation. Specifically we harness the expressiveness and temporal nature of Seq-2-Seq Convolutional neural networks (CNNs) to model explicitly disease detection problem via deep and confident image processing. The work in this paper shows that the uncertainty informed decision making can improve the diagnostic performance considerably. Furthermore, we deploy a Memory Network in order to memorize detected images representing infected cells in historical records. We demonstrate and validate empirically the effectiveness of the proposed framework via extensive experimental and rigorous evaluation on large-scale real world data sets. Experiments across different tasks and datasets show robust generalization, accurate and superior performance of proposed method compared to the well-known state-of-the-art diseases detectors.

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Cited By

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https://doi.org/10.3390/pr13010073
Alsrehin NGupta MAlsmadi IAlrababah S(2023)U2-Net: A Very-Deep Convolutional Neural Network for Detecting Distracted DriversApplied Sciences10.3390/app13211189813:21(11898)Online publication date: 31-Oct-2023
https://doi.org/10.3390/app132111898
Wambura SHuang JLi H(2021)Robust Anomaly Detection in Feature-Evolving Time SeriesThe Computer Journal10.1093/comjnl/bxaa17465:5(1242-1256)Online publication date: 5-Jan-2021
https://doi.org/10.1093/comjnl/bxaa174
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cover image ACM Other conferences

VSIP '20: Proceedings of the 2020 2nd International Conference on Video, Signal and Image Processing

December 2020

108 pages

ISBN:9781450388931

DOI:10.1145/3442705

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 March 2021

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VSIP '20: 2020 2nd International Conference on Video, Signal and Image Processing

December 4 - 6, 2020

Jakarta, Indonesia

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Cited By

Huang YKhabusi S(2025)Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A ReviewProcesses10.3390/pr1301007313:1(73)Online publication date: 1-Jan-2025
https://doi.org/10.3390/pr13010073
Alsrehin NGupta MAlsmadi IAlrababah S(2023)U2-Net: A Very-Deep Convolutional Neural Network for Detecting Distracted DriversApplied Sciences10.3390/app13211189813:21(11898)Online publication date: 31-Oct-2023
https://doi.org/10.3390/app132111898
Wambura SHuang JLi H(2021)Robust Anomaly Detection in Feature-Evolving Time SeriesThe Computer Journal10.1093/comjnl/bxaa17465:5(1242-1256)Online publication date: 5-Jan-2021
https://doi.org/10.1093/comjnl/bxaa174
Wambura SHuang JLi H(2020)Long-range forecasting in feature-evolving data streamsKnowledge-Based Systems10.1016/j.knosys.2020.106405206(106405)Online publication date: Oct-2020
https://doi.org/10.1016/j.knosys.2020.106405

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