Investigation of Ways to Improve the HOG Method in the Classification of Histological Images by Machine Learning Methods

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This study analyzes ways to improve the performance of the histogram of oriented gradients (HOG) method for histological image classification using machine learning methods. We propose applying HOG to each color channel of the image, which improves the extraction of texture features characteristic of histological data. A comparative analysis of the traditional HOG and the improved approach is conducted, including an experimental evaluation of their accuracy and processing time using SVM, RF, DT, KNN, and NB classifiers. The results show that the proposed improvements improve the classification accuracy, which makes the modified HOG method promising for application in biomedical analysis.