Deep learning algorithm for detecting skin diseases with new DWT technology

Timely diagnosis of skin diseases and the importance of their treatment with the discrete wavelet transform technology with deep learning through the classification process to pass through two stages, which are the initial processing with discrete wavelets to start the process of Multi resolution analyses (MRA) to analyze the input image to low pass filter and high pass filter the first represents the approach coefficients low low and the second represents the detail coefficients to be high low low high and high high to perform the two processes of denoising the input image and compressing it. One of the skin diseases is skin cancer, which is considered the most dangerous, but if it is not diagnosed early, it can spread to the rest of the body, leading to death. Therefore, the most dangerous type of cancer is skin cancer, and the death rate due to this cancer is 75 %. In this study, early detection and diagnosis of skin cancer was performed with a new and advanced technique, which is discrete Chebyshev wavelet transform (DCHWT) derived from Chebyshev polynomials (CHP) with mathematical methods to obtain new wavelets to create a new filter from these wavelets. The new filter was used to improve the image of affected skin. With the help of this filter, the affected part of the skin is distinguished using the MATLAB program with high accuracy, through deep learning and training on a new convolutional neural network (CNN) with a proposed filter to distinguish the affected part of the skin, which can be called Convolution neural network discrete Chebyshev wavelet transform (CNNDCHWT) in deep learning, where the accuracy is calculated and the percentage was obtained at 98.58 % in a time of 34 seconds, which proves the efficiency of the proposed theory