Selecting Textural Characteristics of Chest X-Rays for Pneumonia Lesions Classification with the Integrated Gradients XAI Attribution Method

Global texture characteristics are powerful tools for solving medical image classification tasks. There are many such characteristics like Grey-Level Co-occurrence Matrices, Grey-Level Run-Length Matrices, Grey-Level Size Zone Matrices, texture matrices and others. However, not all are important when solving particular image classification tasks, while their calculation requires many computational resources. The current work aims to evaluate the importance of each characteristic, taking into account a large dimensionality of the texture characteristics matrices. To achieve this aim, it is proposed to use neural networks and a novel mean integrated gradient eXplainable Artificial Intelligence method to achieve the stated aim. The experiment showed that texture matrices with higher mean integrated gradient values are more important than others while solving pneumonia lesions classification tasks on X-Ray lung images. The result also indicates that classification quality does not degrade and even improves after shrinking the feature set with the proposed method. These facts prove that the mean integrated gradients can be used for solving feature selection tasks for classification purposes.