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Deep Learning Based Ultrasound Image Classification for Improved and Better Medical Diagnosis
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Ultrasound is the best imaging techniques for detection of abnormalities in the human body. Ultrasound is a medical imaging technique in which a transducer transmits and receives the ultrasound waves to and from the organs of the human body. Ultrasound waves are high-frequency wave ranges from 20 KHz to Giga Hertz. Ultrasound methods are non-invasive, pain-free and patient-friendly techniques. Detection of abnormalities using Ultrasound helps doctors to treat the patient. Abnormalities in liver, Breast, Kidney, Uterus, Heart, Liver, Nerves, Prostate found out using ultrasound techniques has a list of unidentifiable problems in Medical Images in traditional methods. Deep learning plays a vital role in the modern era for much problem identification in medical imaging and other domain.
Keywords
Classification, Convolutional Neural Network, Linear Regression, Medical Imaging, Random Forest.
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- Y. Hiramatsu, C. Muramatsu, H. Kobayashi, T. Hara, and H. Fujita, “Automated detection of masses on whole breast volume ultrasound scanner: False positive reduction using deep convolutional neural network,” In: Proceedings of the SPIE Medical Imaging, Orlando, FL, USA, Bellingham: SPIE, February 11-16, 2017.
- C. Bian, R. Lee, Y. Chou, and J. Cheng, “Boundary regularized convolutional neural network for layer parsing of breast anatomy in automated whole breast ultrasound,” In: M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, S. Duchesne, (ed.), Medical Image Computing and Computer-Assisted Intervention (MICCAI), Berlin: Springer, pp. 259-266, 2017.
- M. Manikandan, N. V. Andrews, and V. Kavitha, “Investigation on micro classification of breast cancer from mammogram image sequence,” International Journal of Pure and Applied Mathematics, vol. 118, no. 20, pp. 645-649, 2018.
- T. Doan, and J. Kalita, “Selecting machine learning algorithms using regression models,” 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015.
- I. Fasel, and J. Berry, “Deep belief networks for realtime extraction of tongue contours from ultrasound during speech,” In: Proceedings of 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey, pp. 1493-1496, August 23-26, 2010.
- S. Keerthi, and S. Dhivya, “Comparison of RVM and SVM classifier performance in analysing the tuberculosis in chest X Ray,” International Journal of Control Theory and Applications, vol. no. 10, no. 36, pp. 269276, 2017.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 25, no. 2, pp. 1097-1105, 2012.
- S. Mohanapriya, and M. Vadivel, “Automatic retrival of MRI brain image using multiqueries system,” 2013 International Conference on Information Communication and Embedded Systems (ICICES), INSPEC Accession Number: 13485254, doi: 10.1109/ ICICES.2013.6508214, pp. 1099-1103, 2013.
- C. Dong, C. L. Chen, and X. Tang, “Accelerating the super-resolution convolutional neural network,” European Conference Computer Vision, pp. 391-407, 2016.
- K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4509-4522, September 2017.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778, 2016.
- M. Annakamatchi, and V. Keralshalini, “Design of spiral shaped patch antenna for bio-medical applications,” International Journal of Pure and Applied Mathematics, vol. 118, no. 11, pp. 131-135, 2018.
- J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?,” In: Advances in Neural Information Processing Systems 27 (NIPS 2014), pp. 3320-3328, 2014.
- S. Azizi, F. Imani, B. Zhuang, A. Tahmasebi, J. T. Kwak, ......, and P. Abolmaesumi, “Ultrasound-based detection of prostate cancer using automatic feature selection with deep belief networks,” In: N. Navab, J. Hornegger, W. Wells, A. Frangi, (ed.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Berlin: Springer, pp. 70-77, 2015.
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