Medical Diagnosis Model based on Spiking Neural Network considering Normalization of Histogram and Wavelet Transform Features of Medical Images

Medical Diagnosis Model based on Spiking Neural Network considering Normalization of Histogram and Wavelet Transform Features of Medical Images
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-7
Year of Publication : 2021
Authors : Ashish Kumar Dehariya, Pragya Shukla
DOI :  10.14445/22315381/IJETT-V69I7P222

How to Cite?

Ashish Kumar Dehariya, Pragya Shukla, "Medical Diagnosis Model based on Spiking Neural Network considering Normalization of Histogram and Wavelet Transform Features of Medical Images," International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 159-166, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I7P223

Abstract
Experts diagnose most diseases by interpreting medical images. The same task can be performed with improved accuracy by automating medical diagnosis algorithms. Convolutional Neural Network (CNN) classifies medical images considering continuous features. Sometimes processing of unnecessary features increases computation time also decreases classification accuracy. This research processed extracted medical image features by Spiking Neural Network(SNN). SNN uses time-based input spikes and overlooks the processing of useless input features hence obtain improved classification accuracy. Second level discrete wavelet transform and histogram features utilized in the proposed approach to prepare a normalized vector for the training of spiking neural network. The experiment was done on real medical image datasets. For each 125 image dataset Spiking Neural Based Medical Image Diagnosis (SNMID) gives an accuracy percentage of 91.20 for malaria, 87.67 for breast cancer, and 100 for skin cancer, whereas CNN based medical image diagnosis(CNMID) lacks the accuracy having measures 57.72 for malaria, 78.05 for breast cancer, 69.12 for skin cancer. Precision, recall, and f-measure values also found improved in the case of SNMID.

Keywords
Convolutional Neural Network, Discrete Wavelet Transform, Histogram Features, Medical Image Diagnosis, Spiking Neural Network.

Reference
[1] Senthilkumar, Gagan Kumar B, and Lasya K R., Artificial Intelligence Augmentation in Blood Transfusion, Biochemistry, and Hematology of Digital Pathology: A Comparative Performance Evaluation on Pathology Labs and Corporate Hospitals located in Bengaluru, International Journal of Engineering Trends and Technology, 68(12) (2020) 132-139.
[2] Muhammad Imran Razzak, Saeeda Naz, and Ahmad Zaib., Deep Learning for Medical Image Processing: Overview, Challenges and Future, Deep Learning for Medical Imaging, (2020).
[3] Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez., A survey on deep learning in medical image analysis, Medical Image Analysis, 42 (2017).
[4] Astha Singh, Asst. Prof. Jayshree Boaddh, Asst. Prof. Jashwant Samar., A Survey on Digital Image Retrieval Technique and Visual Features Authors, IJSRET, 7(1) 2021.
[5] Alexander Selvikvåg Lundervold, Arvid Lundervold., An overview of deep learning in medical imaging focusing on MRI, Zeitschrift für Medizinische Physik, 29(2) (2019).
[6] Mehedi Masud, Hesham Alhumyani, Sultan S. Alshamrani, Omar Cheikhrouhou, Saleh Ibrahim, Ghulam Muhammad, M. Shamim Hossain, Mohammad Shorfuzzaman., Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application, Wireless Communications and Mobile Computing, Article ID 8895429, (2020) 15.
[7] Fuhad, K.M.F.; Tuba, J.F.; Sarker, M.R.A.; Momen, S.; Mohammed, N.; Rahman, T., Deep Learning-Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone-Based Application, Diagnostics, (2020).
[8] P. A. Pattanaik, M. Mittal and M. Z. Khan., Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images, in IEEE Access, 8 (2020) 94936-94946.
[9] Sahni P., Mittal N., Breast Cancer Detection Using Image Processing Techniques. In: Kumar M., Pandey R., Kumar V. (eds) Advances in Interdisciplinary Engineering, Lecture Notes in Mechanical Engineering. Springer, Singapore, (2019).
[10] Y. Wang., Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound, in IEEE Transactions on Medical Imaging, 39(4) (2020) 866-876.
[11] J. Zheng, D. Lin, Z. Gao, S. Wang, M. He, and J. Fan., Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis, in IEEE Access, 8 (2020) 96946-96954.
[12] Shivangi Jain, Vandana jagtap, Nitin Pise., Computer-Aided Melanoma Skin Cancer Detection Using Image Processing, Procedia Computer Science, 48 (2015).
[13] Kalwa, U.; Legner, C.; Kong, T.; Pandey, S. Skin Cancer Diagnostics with an All-Inclusive Smartphone Application, (2019). Symmetry 11, 790.
[14] Mohammad Ashraf Ottom., Convolutional Neural Network for Diagnosing Skin Cancer, International Journal of Advanced Computer Science and Applications, 10(7) (2019).
[15] Divya Srivastava, Rajesh Wadhvani, and Manasi Gyanchandani., A Review: Color Feature Extraction Methods for Content-Based Image Retrieval, IJCEM International Journal of Computational Engineering & Management, 18(3) (2015).
[16] Iqbal H. Sarker., Content-based Image Retrieval Using Haar Wavelet Transform and Color Moment, The Smart Computing Review 3(3) (2013).DOI: 10.6029/smartcr.2013.03.002.
[17] Naoya Muramatsu and Hai-Tao Yu., Combining SNN and ANN for enhanced image classification, Neural and Evolutionary Computing, arXiV.org:2102.10592, (2021).
[18] Taeyoon Kim et al., Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update, Frontiers in Computational science, (2021). https://doi.org/10.3389/fncom.2021.646125
[19] Schliebs, S; Kasabov, N., Evolving spiking neural networks: A Survey, Zurich Open Repository and Archive , Zurich Open Repository and Archive, (2013).
[20] S. G. Wysoski, L. Benuskova, and N. Kasabov., Fast and adaptive network of spiking neurons for multi-view visual pattern recognition, Neurocomputing, 71(13) (2008) 2563–2575.
[21] Taki Hasan Rafi., A Brief Review on Spiking Neural Network - A Biological Inspiration, preprints.org, doi: 10.20944/preprints202104.0202.v1, Online: 7 April 2021
[22] Vijayalakshmi, D., Nath, M.K. & Acharya, O.P., A Comprehensive Survey on Image Contrast Enhancement Techniques in Spatial Domain. Sens Imaging, 21(40) (2020).
[23] Varun Srivastava, Ravindra Kumar Purwar., A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images, Applied Computational Intelligence and Soft Computing, Article ID 9571262, 9 (2017).
[24] AmirhosseinTavanaei, Anthony S. Maida., A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks, (IJARAI) International Journal of Advanced Research in Artificial Intelligence, 4(7) (2015).
[25] Pritam Bose, Nikola K. Kasabov, Lorenzo Bruzzone, and Reggio N. Hartono., Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series, IEEE transactions on geoscience and remote sensing, (2016).
[26] Quan Quan, Jianxin Wang, Liangliang Liu., An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases, Interdisciplinary Sciences: Computational Life Sciences, (2020).