ARCHIVES
Original Article
AI-Based Brain Stroke Detection
Jayant Rohankar1
Sidhesh Bankar2
12IT, St. Vincent Pallotti College of Engineering & Technology/ RTMNU, Nagpur, Maharashtra, India.
Published Online: March-April 2025
Pages: 170-173
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250602023References
1. Maier, O., et al. (2017). ISLES 2015 – A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI .
Medical Image Analysis, 35, 250-269.
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3. Chilamkurthy, S., et al. (2018). Deep learning algorithms for detection of critical findings in head CT scans. The Lancet, 392(10162),
2388–2396.
4. Haarburger, C., et al. (2020). Analysis of uncertainty estimation methods in deep learning for head CT imaging. Medical Image
Analysis, 65, 101747.
5. Pinto, A., et al. (2019). Automated brain lesion segmentation on MRI using CNNs: Comparison with manual segmentation.
NeuroImage: Clinical, 22, 101704.
6. Wood, A., et al. (2020). Data governance in AI medical systems. Journal of Medical Ethics, 46(8), 543-548.
7. Reyes, M., et al. (2020). Interpretability of deep learning in medical imaging. Nature Machine Intelligence, 2(5), 266–273.
8. McKinney, S. M., et al. (2020). International evaluation of AI systems for breast cancer screening. Nature, 577, 89–94.
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10. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29
Medical Image Analysis, 35, 250-269.
2. Sundaresan, V., et al. (2021). Clinical data and imaging fusion for stroke outcome prediction. Scientific Reports, 11, 4024.
3. Chilamkurthy, S., et al. (2018). Deep learning algorithms for detection of critical findings in head CT scans. The Lancet, 392(10162),
2388–2396.
4. Haarburger, C., et al. (2020). Analysis of uncertainty estimation methods in deep learning for head CT imaging. Medical Image
Analysis, 65, 101747.
5. Pinto, A., et al. (2019). Automated brain lesion segmentation on MRI using CNNs: Comparison with manual segmentation.
NeuroImage: Clinical, 22, 101704.
6. Wood, A., et al. (2020). Data governance in AI medical systems. Journal of Medical Ethics, 46(8), 543-548.
7. Reyes, M., et al. (2020). Interpretability of deep learning in medical imaging. Nature Machine Intelligence, 2(5), 266–273.
8. McKinney, S. M., et al. (2020). International evaluation of AI systems for breast cancer screening. Nature, 577, 89–94.
9. Erickson, B. J., et al. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505–515.
10. Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29
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