IBFDS: Intelligent bone fracture detection system

Abstract Rapidly developing technologies are emerging every day in different fields, especially in medical environment. However, still some old techniques are quite popular, efficient and effective in this manner. X-Rays are one of these techniques for detection of bone fractures. Nevertheless, sometimes the size of fractures is not significant and could not be detected easily. Therefore, effective and intelligent systems should be designed. This paper aims to develop an intelligent classification system that would be capable of detecting and classifying the bone fractures. The developed system comprises of two principal stages. In the first stage, the images of the fractures are processed using different image processing techniques in order to detect their location and shapes and the next stage is the classification phase, where a backpropagation neural network is trained and then tested on processed images. Experimentally, the system was tested on different bone fracture images and the results show high efficiency and a classification rate.

[1]  Kamil Dimililer,et al.  Effect of Image Enhancement on MRI Brain Images with Neural Networks , 2016 .

[2]  Majid Abdolshah,et al.  Classification of X-Ray images of shipping containers , 2017, Expert Syst. Appl..

[3]  K. Vidhya,et al.  Development of Medical Image Compression Techniques , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[4]  Nezam Mahdavi-Amiri,et al.  An efficient simplified neural network for solving linear and quadratic programming problems , 2006, Appl. Math. Comput..

[5]  Kamil Dimililer,et al.  Gaussian Noise and Haar Wavelet Transform Image Compression on Transmission of Dermatological Images , 2011, ACC.

[6]  Xiaohong Gao.  Feature wise representation for both still and motion 3D medical images , 2014, 2014 Southwest Symposium on Image Analysis and Interpretation.

[7]  Shabir A. Parah,et al.  A New Method of Haar and Db10 Based Secured Compressed Data Transmission Over GSM Voice Channel , 2017 .

[8]  A. Khashman,et al.  Image compression using neural networks and haar wavelet , 2008 .

[9]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[10]  Kamil Dimililer,et al.  ILTDS: Intelligent Lung Tumor Detection System on CT Images , 2016 .

[11]  Kathiravan Srinivasan,et al.  A hybrid NSCT domain image watermarking scheme , 2017, EURASIP J. Image Video Process..

[12]  Kamil Dimililer,et al.  Backpropagation Neural Network Implementation for Medical Image Compression , 2013, J. Appl. Math..

[13]  Abdul Khader Jilani Saudagar,et al.  Minimize the Percentage of Noise in Biomedical Images Using Neural Networks , 2014, TheScientificWorldJournal.

[14]  Din-Chang Tseng,et al.  Wavelet-based medical image compression with adaptive prediction , 2005, 2005 International Symposium on Intelligent Signal Processing and Communication Systems.

[15]  V. Ravi,et al.  On-Line Evolving Fuzzy Clustering , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[16]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  Adnan Khashman,et al.  Coin identification using neural networks , 2006 .

[18]  Jing Zhang,et al.  An Image Stitching Algorithm Based on Histogram Matching and SIFT Algorithm , 2017, Int. J. Pattern Recognit. Artif. Intell..

[19]  Kamil Dimililer,et al.  Intelligent eye Tumour Detection System , 2016 .

[21]  Keith Price,et al.  Picture Segmentation Using a Recursive Region Splitting Method , 1998 .

[22]  Adnan Khashman,et al.  Iris nevus diagnosis: convolutional neural network and deep belief network , 2017 .

[23]  R. D. Hryciw,et al.  Particulate material fabric characterization by rotational haar wavelet transform , 2017 .

[24]  Long-Wen Chang,et al.  Adaptive Data Hiding for Images Based on Harr Discrete Wavelet Transform , 2006, PSIVT.