Version 1
: Received: 14 May 2024 / Approved: 14 May 2024 / Online: 14 May 2024 (13:07:32 CEST)
How to cite:
Shinde, G.; Goniguntla, S. C.; Shirur, P.; Hambaba, A. DeClEx-Processing Pipeline for Critical Healthcare Application. Preprints2024, 2024050947. https://doi.org/10.20944/preprints202405.0947.v1
Shinde, G.; Goniguntla, S. C.; Shirur, P.; Hambaba, A. DeClEx-Processing Pipeline for Critical Healthcare Application. Preprints 2024, 2024050947. https://doi.org/10.20944/preprints202405.0947.v1
Shinde, G.; Goniguntla, S. C.; Shirur, P.; Hambaba, A. DeClEx-Processing Pipeline for Critical Healthcare Application. Preprints2024, 2024050947. https://doi.org/10.20944/preprints202405.0947.v1
APA Style
Shinde, G., Goniguntla, S. C., Shirur, P., & Hambaba, A. (2024). DeClEx-Processing Pipeline for Critical Healthcare Application. Preprints. https://doi.org/10.20944/preprints202405.0947.v1
Chicago/Turabian Style
Shinde, G., Prajwal Shirur and Ahmed Hambaba. 2024 "DeClEx-Processing Pipeline for Critical Healthcare Application" Preprints. https://doi.org/10.20944/preprints202405.0947.v1
Abstract
Over the last decade, the prevalence of health issues has increased by approximately 29.1%, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly, however many present approaches are unsuitable for real-world implementation due to high memory footprints and lack of interpretability. We introduce DeClEx, a pipeline designed to address these issues. DeClEx ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state of the art pre-trained models while achieving threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
