References
[1]. Flanders A E Prevedello L M Shih G Halabi S S Kalpathy-Cramer J and Nath J 2020 Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge RSNA-ASNR
[2]. Matsoukas S Scaggiante J Schuldt B R et al 2022 Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds Radiol med 127 1106–1123
[3]. O’Neill T J Xi Y Stehel E Browning T Ng Y S, Baker C and Peshock R M 2020 Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage
[4]. Waring J Lindvall C and Umeton R 2020 Automated machine learning: Review of the state-of-the-art and opportunities for healthcare Artificial Intelligence in Medicine 104
[5]. Chauhan K Jani S Thakkar D Dave R Bhatia J Tanwar S and Obaidat M S 2020 Automated Machine Learning: The New Wave of Machine Learning IEEE 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) 205–212
[6]. Mahjoubi mohamed amine 2022 brain-ct-hemorrhage-AMINE-dataset Kaggle brain-ct-hemorrhage-AMINE-dataset Kaggle
[7]. Banbury C Zhou C Fedorov I Matas R Thakker U Gope D Janapa Reddi V Mattina M and Whatmough P 2021 Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers Proceedings of Machine Learning and Systems 3 517–532
[8]. Hymel S Banbury C Situnayake D Elium A Ward C Kelcey M Baaijens M Majchrzycki M Plunkett J Tischler D Grande A Moreau L Maslov D Beavis A Jongboom J and Reddi V J 2023 Edge Impulse: An MLOps Platform for Tiny Machine Learning arXiv:2212.03332v3 [cs.DC]
[9]. Gruenstein A Alvarez R Thornton C and Ghodrat M 2017 A cascade architecture for keyword spotting on mobile devices arXiv preprint arXiv:1712.03603
[10]. Li Z Liu F Yang W Peng S and Zhou J 2021 A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects IEEE Transactions on Neural Networks and Learning Systems
[11]. Hubel D H and Wiesel T N 1962 Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex J. Physiol. 160(1) 106–154
[12]. Qiu Y et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans. In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) pp 112-115 IEEE
[13]. Jin J Dundar A and Culurciello E 2014 Flattened convolutional neural networks for feedforward acceleration arXiv preprint arXiv:1412.5474
[14]. Rastegari M Ordonez V Redmon J and Farhadi A 2016 Xnornet: Imagenet classification using binary convolutional neural networks arXiv preprint arXiv:1603.05279
[15]. Wang M Liu B and Foroosh H 2016 Factorized convolutional neural networks arXiv preprint arXiv:1608.04337
Cite this article
Li,C. (2024). Investigation related to detection of Intracranial Hemorrhage based on edge impulse enhanced CT scanning. Applied and Computational Engineering,41,124-130.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Flanders A E Prevedello L M Shih G Halabi S S Kalpathy-Cramer J and Nath J 2020 Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge RSNA-ASNR
[2]. Matsoukas S Scaggiante J Schuldt B R et al 2022 Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds Radiol med 127 1106–1123
[3]. O’Neill T J Xi Y Stehel E Browning T Ng Y S, Baker C and Peshock R M 2020 Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage
[4]. Waring J Lindvall C and Umeton R 2020 Automated machine learning: Review of the state-of-the-art and opportunities for healthcare Artificial Intelligence in Medicine 104
[5]. Chauhan K Jani S Thakkar D Dave R Bhatia J Tanwar S and Obaidat M S 2020 Automated Machine Learning: The New Wave of Machine Learning IEEE 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) 205–212
[6]. Mahjoubi mohamed amine 2022 brain-ct-hemorrhage-AMINE-dataset Kaggle brain-ct-hemorrhage-AMINE-dataset Kaggle
[7]. Banbury C Zhou C Fedorov I Matas R Thakker U Gope D Janapa Reddi V Mattina M and Whatmough P 2021 Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers Proceedings of Machine Learning and Systems 3 517–532
[8]. Hymel S Banbury C Situnayake D Elium A Ward C Kelcey M Baaijens M Majchrzycki M Plunkett J Tischler D Grande A Moreau L Maslov D Beavis A Jongboom J and Reddi V J 2023 Edge Impulse: An MLOps Platform for Tiny Machine Learning arXiv:2212.03332v3 [cs.DC]
[9]. Gruenstein A Alvarez R Thornton C and Ghodrat M 2017 A cascade architecture for keyword spotting on mobile devices arXiv preprint arXiv:1712.03603
[10]. Li Z Liu F Yang W Peng S and Zhou J 2021 A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects IEEE Transactions on Neural Networks and Learning Systems
[11]. Hubel D H and Wiesel T N 1962 Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex J. Physiol. 160(1) 106–154
[12]. Qiu Y et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans. In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) pp 112-115 IEEE
[13]. Jin J Dundar A and Culurciello E 2014 Flattened convolutional neural networks for feedforward acceleration arXiv preprint arXiv:1412.5474
[14]. Rastegari M Ordonez V Redmon J and Farhadi A 2016 Xnornet: Imagenet classification using binary convolutional neural networks arXiv preprint arXiv:1603.05279
[15]. Wang M Liu B and Foroosh H 2016 Factorized convolutional neural networks arXiv preprint arXiv:1608.04337