Investigation of Histological Image Classification Methods Using Different Feature Extraction Techniques

This paper examines the performance of different machine and deep learning algorithms in classifying colon histological images using different feature extraction methods. The relationship between the feature extraction methods and the selected machine learning methods to improve the classification accuracy is analyzed. Widely used methods like local binary patterns, histograms of oriented gradients, Gabor filter and Dobeshi wavelets are investigated for feature extraction from colon histological images. The features extracted by histogram of oriented gradients and Gabor filter methods were used as a single joint feature vector. And popular machine learning methods such as Support vector machine, Decision trees, Random forest, k-nearest neighbors and Naive Bayesian method were used to classify the selected images. The paper also investigates ensemble methods using gradient bousting and voting classifier as examples. The authors also focus on the study of convolutional neural networks as they are one of the main deep learning methods at the moment. The classification methods selected for analysis are compared in terms of classification accuracy and time taken for training and recognition. All pre-defined and adjustable parameters of both feature extraction methods and classification methods were personally selected by the authors as a result of experimental studies, which were conducted using a software tool created in the Python programming language on a set of LC25000 histological images. The software created is easily customizable and can be used in the future to investigate classification methods on other datasets.