Comparative Analysis of Color Space in Histopathology Image Classification

The classification of histopathological imagery has garnered significant interest among researchers in the last decade due to the valuable outcome that could be obtained from classifying such microscopic fractions. This would significantly contribute to examining biological interactions. To do so, researchers in the literature have employed various machine-learning classification algorithms. However, the key to success for a precise classification task lies in utilizing an appropriate set of features with proper color space channels that can extract important characteristics from the histopathological images. However, the literature shows a limited feature extraction method with limited color space channel utilization. The accuracy of classification is significantly influenced by the color channels. This study aims to extend feature learning by using a wide range of feature extraction methods and different employs distinct color channels to categorize histopathological imagery. It utilizes two benchmark datasets pertinent to the imagery of breast and prostate cancer for the study. Additionally, the study incorporated a series of pre-processing procedures, such as segmenting the images and extracting salient features. Image segmentation in this research was conducted using four distinct methodologies, encompassing Lumen, Nuclei, Cytoplasm, and Stroma. The reason behind selecting such feature extraction methods and color channels is their popularity and differences, which ensure diversity. Finally, the study utilized SVM-RFE to select features and classify images. The assessment employed metrics such as sensitivity, f1-score, and accuracy. The empirical findings demonstrate that the RGB color space yielded superior performance particularly on the sensitivity evaluation metrics across both datasets, underscoring RGB’s efficacy in classifying histopathological images.