Attention-Guided Mask R-CNN for Liver Tumor Segmentation and Classification from CT Scans: A Tailored Approach
Computed tomography (CT) images have poor tissue contrasts, irregular appearance of lesions, and unclear tumor borders which limit reliable diagnosis of liver malignancy. This paper introduces an attention-controlled automated liver tumor segmentation and classification based on a customized Mask Region Convolutional Neural Network (tm-RCNN) with an addition of Multi-Scale Attention Gate (MSAG). To increase the local contrast and reduce artifacts caused by acquisition, adaptive histogram equalization is used. The suggested MSAG selectively elevates boundary-sensitive features in a variety of spatial resolutions, which allow accurate delineation of lesions within the tm-RCNN decoder. Deep residual features, geometric form descriptors and enhanced median binary pattern (e-MBP) textures are extracted using segmented areas and then classified using a hybrid SqueezeNet DeepMaxout ensemble with score-level fusion. It has been experimentally validated on two benchmarking CT sets with a higher performance that achieves a Dice coefficient of 0.9587, classification accuracy of 0.936, sensitivity of 0.961 and less computational time of 64.21 s than the state-of-the-art. These findings affirm the usefulness and clinical appropriateness of the suggested framework.