Computer Aided Diagnosis systems

I need someone to write appr. 8000-10000 words about the main steps of Computer Aided Diagnosis systems. paraphrase is legal but it should be 0 plagiarism. The scientific articles, papers,books should fully referenced I would like to see the sources that will be used as a reference. I may share my sources (paper, articles) as it is required The main steps of CAD systems * Brief history of CAD systems and The challenges of CAd systems Preprocessing Preprocessing is the treatment performed on the image that aims to improve the quality of it to increase the precision and accuracy of processing algorithms that take place after this stage. This stage removes defects caused by the image acquisition process, for example, noise and lack of contrast. the main techniques for preprocessing are Median Filtering Contrast Enhancement Contrast Stretching Gaussian Smoothing Filtering Enhancement Filter Contrast Limited Adaptive Histogram Equalization Wiener filter Fast Fourier Transform Wavelet Transform Erosion Filter Smoothing filters Segmentation of images This subsystem has the function to separate the study region from other organs and tissues in radiographic images in order to reduce the computational cost of the next stages, The main approaches for segmentation of images Threshold based segmentation. Edge based segmentation. Region based segmentation. Clustering techniques. Matching. Feature extraction It is the process of acquiring higher level information of an image, such as color, shape, and texture. Features contain the relevant information of an image and will be used in image processing (e.g. searching, retrieval, storing). Features are divided into different classes based on the kind of properties they describe * describe the methods Classification supervised classification unsupervised classification advantages and disadvantages supervised vs unsupervised briefly mention about the classifiers Nearest-Neighbor Rule Minimum distance classifier Cascade Classifier Bayesian Classifier Radial basis function network (RBF) Explain classifiers ANN & SVM broadly advantages and disadvantages *Artificial Neural Network *SVM