The early sign detection of liver lesions plays an extremely important role in preventing, diagnosing, and treating liver diseases. In fact, radiologists mainly consider Hounsfield Units to locate liver lesions. However, most studies focus on the analysis of unenhanced computed tomography images without considering an attenuation difference between Hounsfield Units before and after contrast injection. Therefore, the purpose of this work is to develop an improved method for the automatic detection and classification of common liver lesions based on deep learning techniques and the variations of the Hounsfield Units density on computed tomography scans. We design and implement a multi-phase classification model developed on the Faster Region-based Convolutional Neural Networks (Faster R–CNN), Region-based Fully Convolutional Networks (R–FCN), and Single Shot Detector Networks (SSD) with the transfer learning approach. The model considers the variations of the Hounsfield Unit density on computed tomography scans in four phases before and after contrast injection (plain, arterial, venous, and delay). The experiments are conducted on three common types of liver lesions including liver cysts, hemangiomas, and hepatocellular carcinoma. Experimental results show that the proposed method accurately locates and classifies common liver lesions. The liver lesions detection with Hounsfield Units gives high accuracy of 100%. Meanwhile, the lesion classification achieves an accuracy of 95.1%. The promising results show the applicability of the proposed method for automatic liver lesions detection and classification. The proposed method improves the accuracy of liver lesions detection and classification compared with some preceding methods. It is useful for practical systems to assist doctors in the diagnosis of liver lesions. In our further research, an improvement can be made with big data analysis to build real-time processing systems and we expand this study to detect lesions from all parts of the human body, not just the liver.

早期发现肝脏病变的体征对于肝脏疾病的预防、诊断和治疗具有极其重要的作用。事实上，放射科医生主要考虑使用亨斯菲尔德单位来定位肝脏病变。然而，大多数研究侧重于分析未增强的计算机断层扫描图像，而没有考虑造影剂注入前后亨斯菲尔德单位之间的衰减差异。因此，这项工作的目的是开发一种改进的方法，基于深度学习技术和计算机断层扫描中亨斯菲尔德单位密度的变化，对常见肝脏病变进行自动检测和分类。我们设计并实现了基于更快的基于区域的卷积神经网络（Faster R-CNN）、基于区域的全卷积网络（R-FCN）开发的多阶段分类模型，和具有迁移学习方法的单次检测器网络（SSD）。该模型考虑了造影剂注射前后四个阶段（普通、动脉、静脉和延迟）计算机断层扫描扫描中亨斯菲尔德单位密度的变化。实验针对三种常见类型的肝脏病变进行，包括肝囊肿、血管瘤和肝​​细胞癌。实验结果表明，所提出的方法可以准确定位和分类常见的肝脏病变。使用 Hounsfield 单位的肝脏病变检测具有 100% 的高精度。同时，病灶分类准确率达到95.1%。有希望的结果表明所提出的自动肝脏病变检测和分类方法的适用性。与之前的一些方法相比，所提出的方法提高了肝脏病变检测和分类的准确性。实用系统有助于医生诊断肝脏病变。在我们进一步的研究中，可以通过大数据分析进行改进以构建实时处理系统，我们将这项研究扩展到检测人体所有部位的病变，而不仅仅是肝脏。