CDANet: Channel Split Dual Attention Based CNN for Brain Tumor Classification In Mr Images

Brain tumor is the most common type of cancer that causes a high mortality rate among individuals of all age groups. Hence, accurate diagnosis of brain tumor and its type at early stages is of utmost importance to preclude its severity and ultimately helps patients for timely and better treatment. The current literature has witnessed the usage of convolutional neural networks (CNN) for brain tumor classification in MR images; however, such traditional CNN methods may fail to identify the minute variations of tumor lesions. This paper proposes an attention module called channel split dual attention (CSDA) coupled with a backbone network to handle this issue. The CSDA explores more detailed and discriminative features using channel splitting and two parallel attention blocks: position attention block (PAB) and channel attention block (CAB). The PAB and CAB are introduced to capture feature dependencies in spatial and channel dimensions. Extensive experiments on a publicly available dataset show that our CDANet significantly improves the state-of-the-art CNN results and obtains higher classification accuracy than existing brain tumor detection methods. The codes and models are available at https://github.com/TapasKumarDutta1/CDANet.