Automatic classification of liver tumors by combining feature reuse and attention mechanism

Abstract

Objective: Liver, which is the largest organ in the abdomen, plays a vital role in the metabolism of the human body. Early detection, accurate diagnosis, and further treatment of liver disease are important and helpful to increase the chances for survival. Computed tomography (CT) is an effective tool to detect focal liver lesions due to its robust and accurate imaging techniques. Multiphase CT scans are generally divided into four phases, namely, noncontrast, arterial, portal, and delay. Radiological practice mainly relies on clinicians to analyze the liver. Physicians need to look back or forward in different phases. This task is time and energy consuming. Liver lesion detection also depends on experienced professional physicians. The identification and diagnosis of different liver lesions are challenging tasks for inexperienced doctors due to the similarity among CT images. Therefore, an effective computer-aided diagnosis (CAD) method for doctors should be designed and developed. Most existing research methods based on CAD are mainly deep learning. Convolutional neural network in deep learning is a data-driven approach, which means it requires much training data to make a model learn the good features for a specific classification. However, a large-scale and well-annotated dataset is extremely difficult to construct due to the lack of data and the cost of labeling data. In accordance with research and analysis, the current methods for liver lesion classification can be divided into two categories, namely, methods based on data and features. The former focuses on expanding data to increase data diversity. The latter mainly studies the way to modify a network to improve the classification ability. These methods have two major drawbacks. First, appearance invariance cannot be controlled when new samples of a specific class are generated. Second, the feature extraction of lesion region cannot be enhanced adaptively. Method: To solve the above-mentioned problems and improve classification performance, this study proposes a novel method for liver tumor classification by combining feature reuse and attention mechanism. Our contributions are threefold. First, we design a feature reuse module to preprocess medical images. We limit the image intensity values of all CT scans to the range of [-100, 400] Hounsfield unit to eliminate the influences of unrelated tissues or organs on the classification of liver lesions in CT images. A new spatial dimension is added to the 2D pixel matrix, and we concatenate three times for the pixel matrix along the new dimension to generate an efficient feature map with a pseudo-RGB channel. We conduct data augmentation of natural images (such as cut, flip, and fill) to expand medical images and increase their diversity. The feature reuse module not only can enhance the overall representation of original image features but also can effectively avoid the overfitting problem caused by a small sample. Second, we introduce the feature extraction module from two aspects, namely, local and global feature extraction. The local feature extraction block is a pixel-to-pixel modeling, which can enhance the extraction of lesion features by generating a weight factor for each pixel. This block mainly includes two branches, namely, trunk and weight. We feed any given feature map into two group convolution layers in the trunk branch to extract deep and high-dimensional features. It is also fed into the weight branch of encoder-decoder to generate a coefficient factor for each pixel. Lesion features are acquired adaptively by weighting the two branches. The global feature extraction block focuses on the relationship among channels. It can generate a weighting factor by pooling spatially to selectively recalibrate the importance of each feature channel. The ways of local and global feature extraction blocks are processed in parallel to fully mine semantic information with data. Third, we use the training strategy of transfer learning to train the proposed classification model. During training, we transfer the same network layer parameters of SENet(squeeze-and-excitation networks) as those of the proposed network model. Result: We perform comprehensive experiments on 514 CT slices from 120 patients to evaluate the proposed method thoroughly. The average classification accuracy of the proposed method is 87.78%, which shows an improvement of 9.73% over the baseline model (SENet34). The classification recall rates of metastasis, hemangioma, hepatocellular carcinoma, and healthy liver tissues are 79.47%, 79.67%,85.73%, and 98.31%, respectively, by using the algorithm in this paper. Compared with the current mainstream classification models, DensNet, SENet, SE_Resnext, CBAM, and SKNet, the proposed model is excellent in many evaluation indicators. The average accuracy, recall rate, precision, F1-score, and area under ROC curve(AUC) are 87.78%, 84.43%, 84.59%, 84.44%, and 97.50%, respectively, by utilizing the proposed architecture. The ablation experiment proves the effectiveness of the proposed design. Conclusion: The feature reuse module can preprocess medical images to alleviate the overfitting problems caused by lack of data. The feature extraction module based on attention mechanism can mine data features effectively. Our proposed method significantly improves the results of the classification of liver tumors in CT images. Thus, it provides a reliable basis for clinical diagnosis and makes computer-assisted diagnosis possible.