International Journal of Emerging Trends in Science & Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Deep Learning Based Ultrasound Image Classification for Improved and Better Medical Diagnosis


Affiliations
1 Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
     

   Subscribe/Renew Journal


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.
User
Subscription Login to verify subscription
Notifications
Font Size

  • 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.


Abstract Views: 358

PDF Views: 0




  • Deep Learning Based Ultrasound Image Classification for Improved and Better Medical Diagnosis

Abstract Views: 358  |  PDF Views: 0

Authors

S. Pradeep
Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
S. Palanivel Rajan
Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India

Abstract


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.

References