Chitkara University Institute of Engineering and Technology, Chitkara University, Patiala 140401, Punjab, India.
Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India.
Sensors (Basel). 2022 Mar 15;22(6):2278. doi: 10.3390/s22062278.
Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively.
气胸是一种导致呼吸系统衰竭、心脏骤停，甚至在极端情况下导致死亡的胸部疾病。胸部 X 射线（CXR）成像技术是诊断气胸的主要诊断成像技术。计算机诊断系统可以检测胸部 X 射线图像中的气胸区域，这在疾病诊断中提供了很大的益处。在本工作中，提出了一种深度学习神经网络模型，用于检测胸部 X 射线图像中的气胸区域。该模型采用了 Mask Regional Convolutional Neural Network（Mask RCNN）框架和基于 ResNet101 的迁移学习作为骨干特征金字塔网络（FPN）。所提出的模型是在由医学成像信息学会与美国放射学院联合准备的气胸数据集上进行训练的。本工作比较了基于 ResNet101 作为 FPN 的提议的 MRCNN 模型与基于 ResNet50 作为 FPN 的传统模型的操作。与基于 ResNet50 作为 FPN 的传统模型相比，所提出的模型的类损失、边界框损失和掩模损失都更低。两个模型均在学习率为 0.0004 和 0.0006 的情况下进行了模拟，分别有 10 和 12 个时期。
