Automatic Detection and Segmentation of Brain Hemorrhage Based on Improved U-Net Model.

Brain hemorrhage is one of the leading causes of death due to the sudden rupture of a blood vessel in the brain, resulting in bleeding in the brain parenchyma. The early detection and segmentation of brain damage are extremely important for prompt treatment. Some previous studies focused on localizing cerebral hemorrhage based on bounding boxes without specifying specific damage regions. However, in practice, doctors need to detect and segment the hemorrhage area more accurately. In this paper, we propose a method for automatic brain hemorrhage detection and segmentation using the proposed network models, which are improved from the U-Net by changing its backbone with typical feature extraction networks, i.e., DenseNet-121, ResNet-50, and MobileNet-V2. The U-Net architecture has many outstanding advantages. It does not need to do too many preprocessing techniques on the original images and it can be trained with a small dataset providing low error segmentation in medical images. We use the transfer learning approach with the head CT dataset gathered on Kaggle including two classes, bleeding and non-bleeding. Besides, we give some comparison results between the proposed models and the previous works to provide an overview of the suitable model for cerebral CT images. On the head CT dataset, our proposed models achieve a segmentation accuracy of up to 99%.

Hounsfield Unit Variations-Based Liver Lesions Detection and Classification Using Deep Learning.

BACKGROUND: Deep learning-based diagnosis systems are useful to identify abnormalities in medical images with the greatly increased workload of doctors. Specifically, the rate of new cases and deaths from malignancies is rising for liver diseases. Early detection of liver lesions plays an extremely important role in effective treatment and gives a higher chance of survival for patients. Therefore, automatic detection and classification of common liver lesions are essential for doctors. In fact, radiologists mainly rely on Hounsfield Units to locate liver lesions but previous studies often pay little attention to this factor. METHODS: In this paper, we propose an improved method for the automatic classification of common liver lesions based on deep learning techniques and the variation of Hounsfield Unit densities on CT images with and without contrast. Hounsfield Unit is used to locate liver lesions accurately and support data labeling for classification. We construct a multi-phase classification model developed on the deep neural networks of Faster R-CNN, R-FCN, SSD, and Mask R-CNN with the transfer learning approach. RESULTS: The experiments are conducted on six scenarios with multi-phase CT images of common liver lesions. Experimental results show that the proposed method improves the detection and classification of liver lesions compared with recent methods because its accuracy achieves up to 97.4%. CONCLUSION: The proposed models are very useful to assist doctors in the automatic segmentation and classification of liver lesions to solve the problem of depending on the clinician's experience in the diagnosis and treatment of liver lesions.

Improving liver lesions classification on CT/MRI images based on Hounsfield Units attenuation and deep learning.

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.