Unified automated deep learning framework for segmentation and classification of liver tumors

Cancer is a devastating and deadly disease, and liver cancer is one of the leading causes of cancer deaths. Early detection of liver tumor is important to choose a treatment plan, get an accurate prognosis, and gain a deep understanding of the tumor to determine its severity. Despite a lot of research, performing automatic segmentation and classification liver tumor is still a challenging task due to the low tissue contrast between the surrounding organs and the deformable shape of the CT image. Therefore, this paper introduces the unified learning multi-task model network for combined automatic liver tumor segmentation and classification. The first step is to build a multi-task deformable attention U-Net (MDAUnet) technique to segment the liver tumor and capture the features for classification. Here, an attention-based deformable module is used instead of convolution to learn the irregular and inconspicuous appearance of tumors by combining context attention and deformable convolution. Further, a residual skip connection is used to avoid duplicate transmission of low-resolution data by introducing a residual path. In the second step, the segmented liver tumor features from MDAUnet are fed into the deep DenseNet (DDNet) model and concatenation layer. Based on the segmented liver tumor features, DDNet learns distinguishable features for classification. The concatenation layer combines the learned features of the MDAUnet and DDNet models for liver tumor classification. Finally, a fully connected layer classifies primary and secondary liver tumors. Therefore, our proposed ULM-net model outperforms single models in terms of precision, F-1 measure, recall, classification accuracy, and kappa coefficient.