Abstract:
Diabetes detection has become an important task for medical practitioners in
India and abroad. Researchers and scientists have been working on this problem
actively. Machine learning has been contributing majorly to systems, techniques and
solutions for diabetes detection problem. Yet there are challenges which remain to be
addressed.
Recently convolution based machine learning techniques have evolved to give
efficient results in various domains. They have shown applicability over range of
problems. So here recent architectures of Convolution based machine learning
models like Convolutional Neural Networks (CNN) and Capsule Networks (CapsNet)
are discussed. Also, application of these recent models is presented here.
Additionally, challenges faced by current Diabetes detection systems are
discussed. Along with these challenges CapsNet architecture for text analytics is
presented. This CapsNet architecture is closest to Diabetes detection problem in
terms of structure and arrangement of data to be handled. Thus in future this
architecture and its variants can be applied for Diabetes detection.
Keywords:
Diabetes detection,Convolutional Neural Networks,CNN,Capsule Networks,CapsNet,
Refference:
I. A. Mackiewicz and W. Ratajczak, “Principal components analysis
(pca),” Computers and Geosciences, vol. 19, pp. 303-342, 1993.
II. A. Mobiny and H. Van Nguyen, “Fast capsnet for lung cancer
screening,” arXiv preprint arXiv:1806.07416, 2018.
III. A. Shahroudnejad, A. Mohammadi, and K. N. Plataniotis,
“Improved explainability of capsule networks: Relevance path by
agreement,” arXiv preprint arXiv:1802.10204, 2018.
IV. B. D. Kanchan and M. M. Kishor, “Study of machine learning
algorithms for special disease prediction using principal of
component analysis,” in Global Trends in Signal Processing,
Information Computing and Communication (ICGTSPICC), 2016
International Conference on, pp. 5-10, IEEE, 2016.
IV. B. Sierra and P. Larranaga, “Predicting survival in malignant skin
melanoma using bayesian networks automatically induced by
genetic algorithms. an empirical comparison between different
approaches,” Artificial Intelligence in Medicine, vol. 14, no. 1-2,
pp. 215-230, 1998.
V. C. Bennett, M. Guo, and S. Dharmage, “Hba1c as a screening tool
for detection of type 2 diabetes: a systematic review,” Diabetic
medicine, vol. 24, no. 4, pp. 333-343, 2007.
VI. C. Willi, P. Bodenmann, W. A. Ghali, P. D. Faris, and J. Cornuz,
“Active smoking and the risk of type 2 diabetes: a systematic review
and meta-analysis,” Jama, vol. 298, no. 22, pp. 2654 -2664, 2007.
VII. D. B. Carr and S. Gabbe, “Gestational diabetes: detection,
management, and implications,” Clinical Diabetes, vol. 16, no. 1,
pp. 4-12, 1998.
IX. Deliege, A. Cioppa, and M. Van Droogenbroeck, “Hitnet: a neural
network with capsules embedded in a hit-or-miss layer, extended
with.
X. D. K. Choubey, S. Paul, S. Kumar, and S. Kumar, “Classification of
pima indian diabetes dataset using naive bayes with genetic
algorithm as an attribute selection,” in Communication and
Computing Systems: Proceedings of the International Conference
on Communication and Computing System (ICCCS 2016), pp. 451-
455, 2017.
XI. Dolz, X. Xu, J. Rony, J. Yuan, Y. Liu, E. Granger, C. Desrosiers, X.
Zhang, I. B. Ayed, and H. Lu, “Multi-region segmentation of
bladder cancer structures in mri with progressive dilated
convolutional networks,” arXiv preprint arXiv:1805.10720, 2018.
XII. dos Santos and M. Gatti, “Deep convolutional neural networks for
sentiment analysis of short texts,” in Proceedings of COLING 2014,
the 25th International Conference on Computational Linguistics:
Technical Papers, pp. 69-78, 2014.
XIII. D. Rawlinson, A. Ahmed, and G. Kowadlo, “Sparse unsupervised
capsules generalize better,” arXiv preprint arXiv:1804.06094, 2018.
XIV. E. Alpaydin, “Introduction to machine learning”. MIT press, 2014.
XV. E. Xi, S. Bing, and Y. Jin, “Capsule network performance on
complex data,” arXiv preprint arXiv:1712.03480, 2017.
XVI. F. Liang, H. Liu, X.Wang, and Y. Liu, “Hyperspectral image
recognition based on artificial neural network,” NeuroQuantology,
vol. 16, no. 5, 2018.
XVII. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally,
and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x
fewer parameters and< 0.5 mb model size,” arXiv preprint
arXiv:1602.07360, 2016.
XVIII. H. Uemura, A. A. Ghaibeh, S. Katsuura-Kamano, M. Yamaguchi,
T. Bahari, M. Ishizu, H. Moriguchi, and K. Arisawa, “Systemic in
ammation and family history in relation to the prevalence of type 2
diabetes based on an alternating decision tree,” Scientific reports,
vol. 7, p. 45502, 2017.
XIX. H. Wu, S. Yang, Z. Huang, J. He, and X. Wang, “Type 2 diabetes
mellitus prediction model based on data mining,” Informatics in
Medicine Unlocked, vol. 10, pp. 100-107, 2018.
XX. H. Ze, A. Senior, and M. Schuster, “Statistical parametric speech
synthesis using deep neural networks,” in Acoustics, Speech and
Signal Processing (ICASSP), 2013 IEEE International Conference
on, pp. 7962{7966, IEEE, 2013.
XXI. Jaiswal, W. AbdAlmageed, and P. Natarajan, “Capsulegan:
Generative adversarial capsule network,” arXiv preprint
arXiv:1802.06167, 2018.
XXII J. Li, G. Li, and H. Fan, “Image dehazing using residual-based deep
cnn,” IEEE Access, 2018.
XXIII J.-S. Jang, “Anfis: adaptive-network-based fuzzy inference system,”
IEEE transactions on systems, man, and cybernetics, vol. 23, no. 3,
pp. 665-685, 1993.
XXIV Kavakiotis, O. Tsave, A. Salifoglou, N. Maglaveras, I. Vlahavas,
and I. Chouvarda, “Machine learning and data mining methods in
diabetes research,” Computational and structural biotechnology
journal, vol. 15, pp. 104-116, 2017.
XXV Khan, I. Yaqoob, I. A. T. Hashem, Z. Inayat, M. Ali, W.
Kamaleldin, M. Alam, M. Shiraz, and A. Gani, “Big data: survey,
technologies, opportunities, and challenges,” The Scientific World
Journal, vol. 2014, 2014.
XXVI K. Kayaer and T. Yldrm, “Medical diagnosis on pima indian
diabetes using general regression neural networks,” in Proceedings
of the international conference on artificial neural networks and
neural information processing (ICANN/ICONIP), pp. 181-184,
2003.
XXVII K. Polat and S. Gunes, “An expert system approach based on
principal component analysis and adaptive neuro-fuzzy inference
system to diagnosis of diabetes disease,” Digital Signal Processing,
vol. 17, no. 4, pp. 702-710, 2007.
XXVIII K. Polat, S. Gunes, and A. Arslan, “A cascade learning system for
classification of diabetes disease: Generalized discriminant analysis
and least square support vector machine,” Expert systems with
applications, vol. 34, no. 1, pp. 482-487, 2008.
XXIX L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement
learning: A survey,” Journal of artificial intelligence research, vol.
4, pp. 237-285, 1996.
XXX M. Engelin, “Capsnet comprehension of objects in different
rotational views,” divaportal.org, 2018.
XXXI M. Fatima and M. Pasha, “Survey of machine learning algorithms
for disease diagnostic,” Journal of Intelligent Learning Systems and
Applications, vol. 9, no. 01, p. 1, 2017.
XXXII M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman,
“Reading text in the wild with convolutional neural networks,”
International Journal of Computer Vision, vol. 116, no. 1, pp. 1-20,
2016.
XXXIII M. Singh, “Classification of diabetic retinopathy stages using deep
learning,” Scientific reports, 2018.
XXXIV P. Afshar, A. Mohammadi, and K. N. Plataniotis, “Brain tumor type
classification via capsule networks,” arXiv preprint
arXiv:1802.10200, 2018.
XXXV Q.Wang, T. Ruan, Y. Zhou, D. Gao, and P. He, “An attention-based
bi-gru-capsnet model for hypernymy detection between compound
entities,” arXiv preprint arXiv:1805.04827, 2018.
XXXVI Q. Xuan, H. Xiao, C. Fu, and Y. Liu, “Evolving convolutional
neural network and its application in fine-grained visual
categorization,” IEEE Access, 2018.
XXXVII R. Johnson and T. Zhang, “Effective use of word order for text
categorization with convolutional neural networks,” arXiv preprint
arXiv:1412.1058, 2014.
XXXVIII R. J. Williams and D. Zipser, “A learning algorithm for continually
running fully recurrent neural networks,” Neural computation, vol.
1, no. 2, pp. 270-280, 1989.
XXXIX R. LaLonde and U. Bagci, “Capsules for object segmentation,”
arXiv preprint arXiv:1804.04241, 2018.
XL. Safran, M. Bloomrosen, W. E. Hammond, S. Labkoff, S. Markel-
Fox, P. C. Tang, and D. E. Detmer, “Toward a national framework
for the secondary use of health data: an american medical
informatics association white paper,” Journal of the American
Medical Informatics Association, vol. 14, no. 1, pp. 1-9, 2007.
XLI. S. Aich and I. Stavness, “Object counting with small datasets of
large images,” arXiv preprint arXiv:1805.11123, 2018.
XLII. Schmidhuber, “Deep learning in neural networks: An overview,”
Neural networks, vol. 61, pp. 85-117, 2015.
XLIII. S. Hochreiter and J. Schmidhuber, “Long short-term memory,”
Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
XLIV. S. Ibrahim, P. Chowriappa, S. Dua, U. R. Acharya, K. Noronha, S.
Bhandary, and H. Mugasa, “Classification of diabetes maculopathy
images using data-adaptive neuro-fuzzy inference classifier,”
Medical & biological engineering & computing, vol. 53, no. 12, pp.
1345-1360, 2015.
XLV. S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between
capsules,” in Advances in Neural Information Processing Systems,
pp. 3856-3866, 2017.
XLVI. S. Valverde, M. Salem, M. Cabezas, D. Pareto, J. C. Vilanova, L.
Ramio-Torrenta, A. Rovira, J. Salvi, A. Oliver, and X. Llado, “Oneshot
domain adaptation in multiple sclerosis lesion segmentation
using convolutional neural networks,” arXiv preprint
arXiv:1805.12415, 2018.
XLVII. T. Deshmukh and H. Fadewar, “Fuzzy deep learning for diabetes
detection,” in Computing, Communication and Signal Processing,
pp. 875-882, Springer, 2019.
XLVIII. W. Liu, E. Barsoum, and J. D. Owens, “Object localization and
motion transfer learning with capsules,” arXiv preprint
arXiv:1805.07706, 2018.
XLIX. X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural
networks,” in Proceedings of the fourteenth international conference
on artificial intelligence and statistics, pp. 315-323, 2011.
L. X. Li, T. Wu, X. Song, and H. Krim, “Aognets: Deep and-or
grammar networks for visual recognition,” arXiv preprint
arXiv:1711.05847, 2017.
LI. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based
learning applied to document recognition,” Proceedings of the
IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
LII. Y. Sebastian, X. T. Tiong, V. Raman, A. Y. Y. Fong, and P. H. H.
Then, “Advances in diabetes analytics from clinical and machine
learning perspectives,” International Journal of Design, Analysis
and Tools for Integrated Circuits and Systems, vol. 6, no. 1, pp. 32-
37, 2017.
LIII. Y. Wang, A. Sun, J. Han, Y. Liu, and X. Zhu, “Sentiment analysis
by capsules,” in Proceedings of the 2018 World Wide Web
Conference on World Wide Web, pp. 1165-1174, International
World Wide Web Conferences Steering Committee, 2018.
LIV. Y. Wang, W. Ke, and P. Wan, “A method of ultrasonic image
recognition for thyroid papillary carcinoma based on deep
convolution neural network,” NeuroQuantology, vol. 16, no. 5,
2018.
LV. Zahangir Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K.
Asari, “Recurrent residual convolutional neural network based on unet
(r2u-net) for medical image segmentation,” arXiv preprint
arXiv:1802.06955, 2018.
LVI. Z. Chen and D. Crandall, “Generalized capsule networks with
trainable routing procedure,” arXiv preprint arXiv:1808.08692,
2018.
LVII. Z. C. Lipton, D. C. Kale, C. Elkan, and R. Wetzel, “Learning to
diagnose with lstm recurrent neural networks,” arXiv preprint
arXiv:1511.03677, 2015.
View
Download
