LibCat » Книги » Приключения » unrecognised » Computational Analysis and Deep Learning for Medical Care

Computational Analysis and Deep Learning for Medical Care

Здесь есть возможность читать онлайн «Computational Analysis and Deep Learning for Medical Care» — ознакомительный отрывок электронной книги совершенно бесплатно, а после прочтения отрывка купить полную версию. В некоторых случаях можно слушать аудио, скачать через торрент в формате fb2 и присутствует краткое содержание. Жанр: unrecognised, на английском языке. Описание произведения, (предисловие) а так же отзывы посетителей доступны на портале библиотеки ЛибКат.

Читать книгу

Название:
Computational Analysis and Deep Learning for Medical Care
Автор:
Неизвестный Автор
Жанр:
unrecognised / на английском языке
Год:
неизвестен
ISBN:
нет данных
Рейтинг книги:
5 / 5. Голосов: 1
Избранное:

Добавить в избранное
Отзывы:
Написать комментарий
Ваша оценка:
- 100
- 1
- 2
- 3
- 4
- 5

Computational Analysis and Deep Learning for Medical Care: краткое содержание, описание и аннотация

Предлагаем к чтению аннотацию, описание, краткое содержание или предисловие (зависит от того, что написал сам автор книги «Computational Analysis and Deep Learning for Medical Care»). Если вы не нашли необходимую информацию о книге — напишите в комментариях, мы постараемся отыскать её.

This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).

Computational Analysis and Deep Learning for Medical Care — читать онлайн ознакомительный отрывок

Ниже представлен текст книги, разбитый по страницам. Система сохранения места последней прочитанной страницы, позволяет с удобством читать онлайн бесплатно книгу «Computational Analysis and Deep Learning for Medical Care», без необходимости каждый раз заново искать на чём Вы остановились. Поставьте закладку, и сможете в любой момент перейти на страницу, на которой закончили чтение.

Тёмная тема

Шрифт:

↓

↑

Сбросить

Интервал:

↓

↑

Закладка:

Сделать

Table 1.10 Comparison of DenseNet.

Figure 111 Architecture of MobileNets - фото 22

Figure 1.11 Architecture of MobileNets.

Table 1.11 Various parameters of MobileNets.

Type/Stride	Filter shape	Input size
Conv / s2	3 × 3 × 3 × 32	224 × 224 × 3
Conv dw / s1	3 × 3 × 32 dw	112 × 112 × 32
Conv / s1	1 × 1 × 32 × 64	112 × 112 × 32
Conv dw / s2	3 × 3 × 64 dw	112 × 112 × 64
Conv / s1	1 × 1 × 64 × 128	56 × 56 × 64
Conv dw / s1	3 × 3 × 128 dw	56 × 56 × 128
Conv / s1	1 × 1 × 128 × 128	56 × 56 × 128
Conv dw / s2	3 × 3 × 128 dw	56 × 56 × 128
Conv / s1	1 × 1 × 1 × 128 × 256	28 × 28 × 128
Conv dw / s1	3 × 3 × 256 dw	28 × 28 × 256
Conv / s1	1 × 1 × 256 × 256	28 × 28 × 256
Conv dw / s2	3 × 3 × 256 dw	28 × 28 × 256
Conv / s1	1 × 1 × 256 × 512	14 × 14 × 256
5 × Conv dw / s1 Conv / s1	3 × 3 × 512 dw	14 × 14 × 512
1 × 1 × 512 × 512	14 × 14 × 512
Conv dw / s2	3 × 3 × 512 dw	14 × 14 × 512
Conv / s1	1 × 1 × 512 × 1024	7 × 7 × 512
Conv dw / s2	3 × 3 × 1,024 dw	7 × 7 × 1,024
Conv / s1	1 × 1 × 1,024 × 1024	7 × 7 × 1,024
Avg Pool / s1	Pool 7 × 7	7 × 7 × 1,024
FC / s1	1024 × 1,000	1 × 1 × 1,024
Softmax / s1	Classifier	1 × 1 × 1,000

Table 1.12 State-of-art of spine segmentation approaches.

Author	Method/Algorithm	Parameters
Mader [11]	V-Net	MDSC (%) = 89.4MASD (mm) = 0.45
Bateson [12]	Constrained domain adaptation employ ENet	MDSC (%) = 81.1HD (mm) = 1.09
Zeng [13]	CNN	MDSC (%)= 90.64MASD (mm) = 0.60
Chang Liu [14]	2.5D multi-scale FCN	MDSC (%) = 90.64MASD (mm) = 0.60MLD (mm) = 0.77
Gao [15]	2D CNN, DenseNet	MDSC (%) = 90.58MASD (mm) = 0.61MLD (mm) = 0.78
Jose [17]	HD-UNet asym	MDSC (%) = 89.67MASD (mm) = 0.65MLD (mm) = 0.964
Claudia Iriondo [16]	VNet-based 3D connected component analysis	MDSC (%) = 89.71MASD (mm) = 0.74MLD (mm) = 0.86

References

1. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE , 86, 11, 2278–2323, 1998.

2. Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks. Commun. ACM , 60, 6, 84–90, 2017.

3. Zeiler, M.D. and Fergus, R., Visualizing and understanding convolutional networks. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics , 8689 LNCS, PART 1, 818–833, 2014.

4. Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition, 3rd Int. Conf. Learn. Represent. ICLR 2015 -Conf. Track Proc ., 1–14, 2015.

5. Szegedy, C. et al ., Going deeper with convolutions. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit . 07-12-June, 1–9, 2015.

6. He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit . 2016-Decem, 770–778, 2016.

7. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K., Aggregated residual transformations for deep neural networks. Proc. -30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 . 2017-Janua, 5987–5995, 2017.

8. Hu, J., Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf, CVPR . 7132–7141, 2018.

9. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., Densely connected convolutional networks. Proc. -30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 , 2017-Janua, 2261–2269, 2017.

10. Howard, A.G. et al ., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017.

11. Mader, A.O., Lorenz, C., Meyer, C., Segmenting Labeled Intervertebral Discs in Multi Modality MR Images. Springer Computational Methods and Clinical Applications for Spine Imaging: 5th International Workshop and Challenge, CSI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, 3, 11397, 178–180, 2019.

12. Bateson, M., Kervadec, H., Dolz, J., Lombaert, H., Ben Ayed, I., Constrained Domain Adaptation for Segmentation. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) , 11765 LNCS, 326–334, 2019.

13. Zeng, G., Belavy, D., Li, S., Zheng, G., Evaluation and comparison of automatic intervertebral disc localization and segmentation methods with 3D multi-modality MR images: A grand challenge. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) , 11397 LNCS, 163–171, 2019.

14. Liu, C. and Zhao, L., Intervertebral disc segmentation and localization from multi-modality MR images with 2.5D multi-scale fully convolutional network and geometric constraint post-processing. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) , 11397 LNCS, 144–153, 2019.

15. Gao, Y., Deep learning framework for fully automated intervertebral disc localization and segmentation from multi-modality MR images. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) , 11397 LNCS, 119–129, 2019.

16. Iriondo, C. and Girard, M., Vesalius: VNet-Based Fully Automatic Segmentation of Intervertebral Discs in Multimodality MR Images. Springer Computational Methods and Clinical Applications for Spine Imaging: 5th International Workshop and Challenge, CSI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 11397, 175–177, 2019.

17. Dolz, J., Desrosiers, C. and Ayed, I.B., IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal Unet, Springer International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, 11397, 130–143, 2018.

1 *Corresponding author: leenasilvoster@gmail.com

Location-Aware Keyword Query Suggestion Techniques With Artificial Intelligence Perspective

R. Ravinder Reddy1*, C. Vaishnavi1, Ch. Mamatha2 and S. Ananthakumaran3

1 Chaitanya Bharathi Institute of Technology, Hyderabad, India