ARM-Net: Attention-guided residual multiscale CNN for multiclass brain tumor classification using MR images

Abstract

Brain tumor is the deadliest type of cancer and has the lowest survival rate when compared with other cancers. Hence, timely detection of brain tumor is indispensable for patients to make better treatment plans, leading to improved life expectancy. However, accurate classification of different brain tumor types from MR images is challenging due to high inter-class similarities. Though deep learning architectures, mainly CNNs, have shown promising performance compared to traditional approaches, such models often demand huge parameters and lead to overfitting while dealing with limited training samples. Further, the state-of-the-art CNN models cannot capture the subtle lesion size and shape variations among different classes. To cope with these issues, in this paper, we propose an attention-based residual multiscale CNN called ARM-Net for multiclass brain tumor classification. In particular, we propose a lightweight residual multiscale CNN dubbed RM-Net to capture high-level feature representations at different receptive fields. Further, a lightweight global attention module (LGAM) is proposed to selectively learn more discriminative features. The LGAM is placed on the top of RM-Net and is introduced to capture wide-range feature dependencies. Experimental results on two benchmark datasets indicate the superiority of our ARM-Net over the state-of-the-art CNN architectures and existing methods. The ARM-Net achieves an accuracy of 96.64% and 97.11% on MBTD and BraTS 2020 dataset, respectively. The ablation studies, Grad-CAM, and Grad-CAM++ visualization results confirm the effectiveness of our proposed LGAM. In addition, our ARM-Net is lightweight, end-to-end learnable, and hence more suitable for real-time brain tumor classification.