Computer aided medical image analysis for capsule endoscopy using conventional machine learning and deep learning

Large population of the world suffers from diseases related to gastrointestinal (GI) tract. The most modern technique available to scan the GI tract is capsule endoscopy (CE). It is a non-sedative, non-invasive and patient-friendly alternative to conventional endoscopy. However, CE generates approximately 55000 to 60000 images which make the diagnosis process time consuming and tiresome for physicians. Also the diagnosis varies from expert to expert. Hence a computer-aided diagnosis system is a must. This study, addresses a multi-class medical image analysis problem using image processing and machine learning techniques. It presents a computer aided diagnosis (CAD) system based on conventional machine learning as well as deep learning for automatic abnormality detection in GI tract. The system performs with an accuracy of 95.11% and precision of 93.9%.

[1]  A. Hussain,et al.  Feature Extraction Technique using Discrete Wavelet Transform for Image Classification , 2007, 2007 5th Student Conference on Research and Development.

[2]  Jung-Hwan Oh,et al.  Abnormal image detection in endoscopy videos using a filter bank and local binary patterns , 2014, Neurocomputing.

[3]  Yong Zhao,et al.  Clothing image retrieval using color moment , 2013, Proceedings of 2013 3rd International Conference on Computer Science and Network Technology.

[4]  Yasushi Yagi,et al.  Detection of contractions in adaptive transit time of the small bowel from wireless capsule endoscopy videos , 2009, Comput. Biol. Medicine.

[5]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[6]  Ramin Zabih,et al.  Comparing images using color coherence vectors , 1997, MULTIMEDIA '96.

[7]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[8]  Guozheng Yan,et al.  Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy , 2016, Comput. Biol. Medicine.

[9]  M. Y. Mashor,et al.  Local and Global Contrast Stretching For Color Contrast Enhancement on Ziehl-Neelsen Tissue Section Slide Images , 2008 .

[10]  Yasushi Yagi,et al.  Abnormality tracking during video capsule endoscopy using an affine triangular constraint based on surrounding features , 2017, IPSJ Transactions on Computer Vision and Applications.

[11]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[12]  H.-B. Shen,et al.  Using ensemble classifier to identify membrane protein types , 2006, Amino Acids.

[13]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Huaici Zhao,et al.  Computer-Aided Endoscopic Diagnosis Without Human-Specific Labeling , 2016, IEEE Transactions on Biomedical Engineering.

[15]  Joni-Kristian Kämäräinen.  Gabor features in image analysis , 2012, IPTA.

[16]  Asadollah Shahbahrami,et al.  Improving visual quality in wireless capsule endoscopy images with contrast-limited adaptive histogram equalization , 2015, 2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA).

[17]  Rajeev Srivastava,et al.  Improved image retrieval using fast Colour-texture features with varying weighted similarity measure and random forests , 2017, Multimedia Tools and Applications.

[18]  Ngo Quoc Tao,et al.  Combining Color and Spatial Information for Retrieving Landscape Images , 2008, 2008 Congress on Image and Signal Processing.

[19]  Prashant Dahiwale,et al.  A combined effect of local and global method for contrast image enhancement , 2015, 2015 IEEE International Conference on Engineering and Technology (ICETECH).

[20]  Hassan Ghassemian,et al.  Using GLCM and Gabor filters for classification of PAN images , 2013, 2013 21st Iranian Conference on Electrical Engineering (ICEE).

[21]  Max Q.-H. Meng,et al.  A general framework for wireless capsule endoscopy study synopsis , 2015, Comput. Medical Imaging Graph..

[22]  Dimitris K. Iakovidis,et al.  Video-based measurements for wireless capsule endoscope tracking , 2014 .