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Computer-aided diagnosis (CADx) systems have been shown to assistradiologists by providing classifications of all kinds of medical images likeComputed tomography (CT) and Magnetic resonance (MR). Currently, convolutionalneural networks play an important role in CADx. However, since CNN model shouldhave a square-like input, it is usually difficult to directly apply the CNNalgorithms on the irregular segmentation region of interests (ROIs) where theradiologists are interested in. In this paper, we propose a new approach toconstruct the model by extracting and converting the information of theirregular region into a fixed-size Gray-Level Co-Occurrence Matrix (GLCM) andthen utilize the GLCM as one input of our CNN model. In this way, as an usefulimplementary to the original CNN, a couple of GLCM-based features are alsoextracted by CNN. Meanwhile, the network will pay more attention to theimportant 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. Inconclusion, the proposed framework outperforms the corresponding state-of-artalgorithms on each database with both test losses and classification accuracyas the evaluation criteria.

A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in Intracerebral Hemorrhage