IET Computer Vision (Mar 2023)
Fine‐grained classification of intracranial haemorrhage subtypes in head CT scans
Abstract
Abstract Intracranial haemorrhage (ICH) is a haemorrhagic disease that occurs in the ventricle or brain tissue and has a high probability of mortality and disability. For ICH, it is important to obtain a correct diagnosis in the early stages. Currently, ICH classification mainly depends on professional radiologists for manual diagnosis. Therefore, it is necessary to develop a method that can efficiently and rapidly diagnose ICH. In the field of ICH subtype classification, most studies directly use the existing convolutional neural network (CNN) to extract CT slice features. However, these existing networks have the following shortcomings: (1) insufficient discrimination of CT slice features leads to an inability to achieve satisfactory classification performance. (2) Most CT slice data sets of ICH have the serious problem of sample imbalance. (3) There is a correlation between subtypes; however, in previous studies, this correlation has been ignored. To solve these problems, the authors propose a classification algorithm for ICH subtypes applied to CT images. The CNN–RNN architecture was adopted to classify ICH subtypes. In the CNN module, the problem is viewed from a fine‐grained perspective, which solves the problem of insufficient feature discrimination in existing methods. A new loss function is also proposed to solve the problems of unbalanced data distribution and neglected dependencies among the labels. These parts are integrated into the proposed fine‐grained network architecture. The image embeddings were obtained by the CNN module and then input to the RNN module. The authors’ method was evaluated on the Radiological Society of North America 2019 Brain CT Haemorrhage (RSNA‐2019) benchmark. The experimental results demonstrated that the performance of the proposed method is state‐of‐the‐art.
Keywords
