Naive Bayes classifier-based dermatosis image color feature extraction method
A Bayesian classifier and color feature technology, applied in the field of skin disease image processing, can solve problems such as reducing the dimension of data processing, and achieve the effect of high classification accuracy, fast speed and high accuracy
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[0055] Computer-aided diagnosis of skin diseases needs to extract the color features of skin disease images, that is, to classify and count the colors of the pixels of skin disease images. This solution builds a color histogram based on a naive Bayesian classifier.
[0056] Bayesian classifiers are based on statistical methods. Suppose, X is an eigenvector, ω iis a category set (i is the index number of the category), then in ω i The probability of X appearing in the set is: Among them, p(ω i |X) is the posterior probability, p(ω i ) is the prior probability of sample data, probability p(X)=∑ i p(X|ω i )p(ω i ), p(X|ω i ) is the conditional probability of the feature attribute.
[0057] If X is classified, it is first necessary to calculate p(ω i |X), and then find all p(ω i |X), X can be classified into ω i kind. Assuming that the same amount of training data is provided for each class, there is a property of the prior probability: for all i and j, there is p(ω ...
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