论文标题
使用U-NET的深卷卷神经网络检测心脏肿大检测
Cardiomegaly Detection using Deep Convolutional Neural Network with U-Net
论文作者
论文摘要
心脏肿大确实是一种心脏肿大的医学疾病。如果提早被发现，心脏肿大最好处理，因此早期发现至关重要。数十年来，胸部X射线是最常使用的X射线照相检查之一，一直用于检测和可视化人体器官异常。 X射线也是心脏肿瘤的重要医学诊断工具。即使对于领域专家，将许多类型的疾病与X射线区分开是一项艰巨且耗时的任务。深度学习模型在巨大的数据集时也是最有效的，但是由于隐私问题，大型数据集在医疗行业内部很少可用。这项研究介绍了一种基于学习的基于学习的定制的u-NET模型，用于检测心脏肿瘤疾病。在训练阶段，使用了来自“ ChestX-Ray8”开源真实数据集的胸部X射线图像。为了减少计算时间，此模型在进行训练步骤之前，在进行数据预处理，图像改进，图像压缩和分类。这项工作使用胸部X射线图像数据集模拟并产生了94％的诊断准确性，灵敏度为96.2％，特异性为92.5％，这比先前训练的识别心脏全肿瘤疾病的预训练模型发现。
Cardiomegaly is indeed a medical disease in which the heart is enlarged. Cardiomegaly is better to handle if caught early, so early detection is critical. The chest X-ray, being one of the most often used radiography examinations, has been used to detect and visualize abnormalities of human organs for decades. X-ray is also a significant medical diagnosis tool for cardiomegaly. Even for domain experts, distinguishing the many types of diseases from the X-ray is a difficult and time-consuming task. Deep learning models are also most effective when used on huge data sets, yet due to privacy concerns, large datasets are rarely available inside the medical industry. A Deep learning-based customized retrained U-Net model for detecting Cardiomegaly disease is presented in this research. In the training phase, chest X-ray images from the "ChestX-ray8" open source real dataset are used. To reduce computing time, this model performs data preprocessing, picture improvement, image compression, and classification before moving on to the training step. The work used a chest x-ray image dataset to simulate and produced a diagnostic accuracy of 94%, a sensitivity of 96.2 percent, and a specificity of 92.5 percent, which beats prior pre-trained model findings for identifying Cardiomegaly disease.
