Kidney stones detection based on deep learning and discrete wavelet transform

Abstract

<p>The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).</p>

Improvement of color image analysis using a hybrid artificial intelligence algorithm

“…Discrete Hermit Wavelet Transform (DHWT) built new discrete wavelets in addition to those previously used, which are the fundamental discrete wavelets such as Haar and others, which were used in image processing for removing noise from the color image and compressing the image and artificial intelligence was used to identify people after creating the convolutional neural network, and accuracy was achieved [24,25]. The rapid technique was developed to achieve the necessary accuracy with wavelets so that neural networks can be used to identify vehicles on the roads and the technique has also been used in medicine to identify kidney stones [20,[26][27][28][29].…”