Medical Image Data Classification Using Deep Machine Learning Techniques and Neural Networks
Keywords:
Copolymerization, Linalool, Methyl methacrylate, Reactivity ratio.Abstract
Radical copolymerization methodology was explored by and making use of acyclic terpenoids namely linalool with methyl methacrylate (MMA) initiated by p-acetyl benzylidene triphenyl arsonium ylide (p-ABTAY) in xylene at 80±1 oC for 50 minutes under inert atmosphere of N2. The reaction results in alternating copolymer as evidenced from reactivity ratios. The system follows ideal kinetics . The overall activation energy is 93.0 kJ / mol . Alfrey Price, Q - e parameters for linalool has been evaluated as 5.02 and -2.30. The copolymer has been characterized by 1H-NMR, FTIR and TGA.
References
- Cruz-Roa, A., Caicedo, J.C., González, F.A. (2011). Visual pattern mining in histology image collections using bag of features. Artificial Intelligence in Medicine, 52(2): 91-106. https://doi.org/10.1016/j.artmed.2011.04.010
- Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639): 115-118. https://doi.org/10.1038/nature21056
- Zare, M.R., Mueen, A., Awedh, M., Seng, W.C. (2013). Automatic classification of medical X-ray images: Hybrid generative-discriminative approach. IET Image Processing, 7(5): 523-532. https://doi.org/10.1049/iet-ipr.2013.0049
- Hinton, G.E., Osindero, S., Teh, Y.W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
- Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 1097-1105. https://doi.org/10.1145/3065386
- Simonyan, K., Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. http://arxiv.org/abs/1409.1556.
- Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S. (2014). Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal, 8(3): 965-979. https://doi.org/10.1109/JSYST.2013.2271540
- Iyatomi, H., Oka, H., Celebi, M.E., Hashimoto, M., Hagiwara, M., Tanaka, M., Ogawa, K. (2008). An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Computerized Medical Imaging and Graphics, 32(7): 566-579. https://doi.org/10.1016/j.compmedimag.2008.06.005
- Stoecker, W.V., Wronkiewiecz, M., Chowdhury, R., Stanley, R.J., Bangert, A., Shrestha, B., Calcara, D.A., Rabinovitz, H.S., Oliviero, M., Ahmed, F., Perry, L.A., Drugge, R. (2011). Detection of granularity in dermoscopy images of malignant melanoma using color and texture features. Computerized Medical Imaging and Graphics, 35(2): 144-147. https://doi.org/10.1016/j.compmedimag.2010.09.005
- Riaz, F., Hassan, A., Nisar, R., Dinis-Ribeiro, M., Coimbra, M. (2017). Content-adaptive region-based color texture descriptors for medical images. IEEE Journal of Biomedical and Health Informatics, 21(1): 162-171. https://doi.org/10.1109/JBHI.2015.2492464
- Ramirez, J., Gorriz, J.M., Segovia, F., Chaves, R., Salas-Gonzalez, D.S., López, M., Alvarez, I., Padilla, P. (2010). Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neuroscience Letters, 472(2): 99-103. https://doi.org/10.1016/j.neulet.2010.01.056
- Zhang, Y., Chen, S., Wang, S., Yang, J., Phillips, P. (2015). Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. International Journal of Imaging Systems and Technology, 25(4): 317-327. https://doi.org/10.1002/ima.22144
- Zhang, Y., Dong, Z., Liu, A., Wang, S., Ji, G., Zhang, Z., Yang, J. (2015). Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. Journal of Medical Imaging and Health Informatics, 5(7): 1395-1403. https://doi.org/10.1166/jmihi.2015.1542
- Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M. (2014). Medical image classification with convolutional neural network. 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, pp. 844–848. https://doi.org/10.1109/ICARCV.2014.7064414
- Bar, Y., Diamant, I., Wolf, L., Greenspan, H. (2015). Deep learning with non-medical training used for chest pathology identification. Proceedings of SPIE Medical Imaging, International Society for Optics and Photonics, Orlando, FL, USA, February 2015, p. 94140V. https://doi.org/10.1117/12.2083124
- Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, RM. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5): 1285-1298. https://doi.org/10.1109/TMI.2016.2528162
- Ahn, E., Kumar, A., Kim, J., Li, C., Feng, D., Fulham, M. (2016). X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, pp. 855-858. https://doi.org/10.1109/ISBI.2016.7493400
- Ouyang, W., Luo, P., Zeng, X., Qiu, S., Tan, Y., Li, H., Yang, S., Wang, Z., Xiong, Y., Qian, C., Zhu, Z., Wang, R., Loy, C., Wang, X., Tang, X. (2014). DeepID-net: multi-stage and deformable deep convolutional neural networks for object detection. http://arxiv.org/abs/1409.3505.
- Patil, S., Kuchanur, M. (2012). Lung cancer classification using image processing. International Journal of Engineering and Innovative Technology(IJEIT), 2(3): 37-42
- Fernando, B., Fromont, E., Muselet, D., Sebban, M. (2012). Discriminative feature fusion for image classification. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 3434-3441. 651 https://doi.org/10.1109/CVPR.2012.6248084
- Chan, T., Jia, K., Gao, S., Lu, J., Zeng, Z., Ma, Y. (2015).PCANet: A simple deep learning baseline for imageclassification? IEEE Transactions on Image Processing,24(12): 5017-5032.https://doi.org/10.1109/TIP.2015.2475625
- Zeng R., Wu, J., Shao, Z., Chen, Y., Chen, B., Senhadji,L., Shu, H. (2016). Color image classification via quaternion principal component analysis network. Neurocomputing, 216: 416-428. https://doi.org/10.1016/j.neucom.2016.08.006
- Rakotomamonjy, A., Petitjean, C., Salaun, M.,Thiberville, L. (2014). Scattering features for lung cancerdetection in fibered confocal fluorescence microscopyimages. Artificial Intelligence in Medicine, 61(2): 105-118.https://doi.org/10.1016/j.artmed.2014.05.003
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST

This work is licensed under a Creative Commons Attribution 4.0 International License.