Brain Cancer Diagnosis Based on Histopathological Images Using Handcrafted Features

Traditionally, pathologists have used a light microscope to examine tissue sections mounted on glass slides to diagnose and categorize brain tumors. The outcomes of this method may not always be perfectly correct and are labor- and time-intensive. The procedure of classifying tumors could be made better using the computer-aided method. In this paper, we present an automated procedure that combines the following three feature extraction techniques to classify histopathological images of brain tumors: color histogram, Hu invariant moments, and scale-invariant hybrid image descriptor (RSHD). Color normalization and augmentation techniques are used for preprocessing the histopathological images before the feature extraction phase. In order to classify brain tumor images, three feature sets were extracted. Next, the generated features were fused together to form a combined feature set fed into the machine learning classifier. For differentiating between different sub-types of brain cancer, the XGBoost model performs best in terms of classification accuracy, precision, F1 score, and recall. XGBoost obtained 97.5% for recall, 94.1% for F1 score, 93.1% for precision, and 92.7% for accuracy. The proposed method produced effective results, outperforming multiple state-of-the-art investigations.