Medical Diagnosis Model based on Spiking Neural Network considering Normalization of Histogram and Wavelet Transform Features of Medical Images
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
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.
Convolutional Neural Network, Discrete Wavelet Transform, Histogram Features, Medical Image Diagnosis, Spiking Neural Network.
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