  Computer-aided diagnosis (CADx) systems have been shown to assist
radiologists by providing classifications of all kinds of medical images like
Computed tomography (CT) and Magnetic resonance (MR). Currently, convolutional
neural networks play an important role in CADx. However, since CNN model should
have a square-like input, it is usually difficult to directly apply the CNN
algorithms on the irregular segmentation region of interests (ROIs) where the
radiologists are interested in. In this paper, we propose a new approach to
construct the model by extracting and converting the information of the
irregular region into a fixed-size Gray-Level Co-Occurrence Matrix (GLCM) and
then utilize the GLCM as one input of our CNN model. In this way, as an useful
implementary to the original CNN, a couple of GLCM-based features are also
extracted by CNN. Meanwhile, the network will pay more attention to the
important lesion area and achieve a higher accuracy in classification.
Experiments are performed on three classification databases: Hemorrhage,
BraTS18 and Cervix to validate the universality of our innovative model. In
conclusion, the proposed framework outperforms the corresponding state-of-art
algorithms on each database with both test losses and classification accuracy
as the evaluation criteria.
