Shamrat Fm Javed Mehedi, Azam Sami, Karim Asif, Ahmed Kawsar, Bui Francis M, De Boer Friso
Department of Software Engineering, Daffodil International University, Birulia, 1216, Dhaka, Bangladesh.
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.
Comput Biol Med. 2023 Mar;155:106646. doi: 10.1016/j.compbiomed.2023.106646. Epub 2023 Feb 10.
In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis. Initially, pre-processing is done on the X-ray images from the dataset using CLAHE to increase image contrast. Additionally, a Gaussian Filter, to denoise images, and data augmentation methods are used. The pre-processed images are fed into several transfer learning models; such as InceptionV3, AlexNet, DenseNet121, VGG19, and MobileNetV2. Among these models, MobileNetV2 performed with the highest accuracy of 91.6% in overall classifying lesions on Chest X-ray Images. This model is then fine-tuned to optimise the MobileLungNetV2 model. On the pre-processed data, the fine-tuned model, MobileLungNetV2, achieves an extraordinary classification accuracy of 96.97%. Using a confusion matrix for all the classes, it is determined that the model has an overall high precision, recall, and specificity scores of 96.71%, 96.83% and 99.78% respectively. The study employs the Grad-cam output to determine the heatmap of disease detection. The proposed model shows promising results in classifying multiple lesions on Chest X-ray images.
在本研究中，借助神经网络算法诊断多种肺部疾病。具体而言，从ChestX-ray14数据集中研究了肺气肿、浸润、肿块、胸膜增厚、肺炎、气胸、肺不张、水肿、胸腔积液、疝、心脏肥大、肺纤维化、结节和实变。采用提出的微调MobileLungNetV2模型进行分析。首先，使用对比度受限自适应直方图均衡化（CLAHE）对数据集中的X射线图像进行预处理，以增加图像对比度。此外，使用高斯滤波器对图像去噪，并采用数据增强方法。将预处理后的图像输入到几个迁移学习模型中，如InceptionV3、AlexNet、DenseNet121、VGG19和MobileNetV2。在这些模型中，MobileNetV2在胸部X光图像上对病变进行总体分类时的准确率最高，为91.6%。然后对该模型进行微调，以优化MobileLungNetV2模型。在预处理数据上，微调后的模型MobileLungNetV2实现了96.97%的非凡分类准确率。使用所有类别的混淆矩阵，确定该模型的总体高精度、召回率和特异性得分分别为96.71%、96.83%和99.78%。该研究采用Grad-cam输出确定疾病检测的热图。所提出的模型在胸部X光图像上对多种病变进行分类方面显示出有前景的结果。
