Thakur Kavita, Kaur Manjot, Kumar Yogesh
Desh Bhagat University, Mandi Gobindgarh, Punjab India.
Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India.
Arch Comput Methods Eng. 2023 Jun 8:1-21. doi: 10.1007/s11831-023-09952-7.
Artificial intelligence is the most powerful and promising tool for the present analytic technologies. It can provide real-time insights into disease spread and predict new pandemic epicenters by processing massive amount of data. The main aim of the paper is to detect and classify multiple infectious diseases using deep learning models. The work is conducted by using 29,252 images of COVID-19, Middle East Respiratory Syndrome Coronavirus, Pneumonia, normal, Severe Acute Respiratory Syndrome, tuberculosis, viral pneumonia, and lung opacity which has been collected from various disease datasets. These datasets are used to train the deep learning models such as EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, NASNetLarge, DenseNet169, ResNet152V2, and InceptionResNetV2. The images have been initially graphically represented using exploratory data analysis to study the pixel intensity and find anomalies by extracting the color channels in an RGB histogram. Later, the dataset has been pre-processed to remove noisy signals using image augmentation and contrast enhancement techniques. Further, feature extraction techniques such as morphological values of contour features and Otsu thresholding have been applied to extract the feature. The models have been evaluated on the basis of various parameters, and it has been discovered that during the testing phase, the InceptionResNetV2 model generated the highest accuracy of 88%, best loss value of 0.399, and root mean square error of 0.63.
人工智能是当前分析技术中最强大且最具前景的工具。它可以通过处理海量数据，提供对疾病传播的实时洞察，并预测新的大流行中心。本文的主要目的是使用深度学习模型检测和分类多种传染病。这项工作是通过使用从各种疾病数据集中收集的29252张新冠肺炎、中东呼吸综合征冠状病毒、肺炎、正常、严重急性呼吸综合征、肺结核、病毒性肺炎和肺不透明的图像来进行的。这些数据集用于训练深度学习模型，如EfficientNetB0、EfficientNetB1、EfficientNetB2、EfficientNetB3、NASNetLarge、DenseNet169、ResNet152V2和InceptionResNetV2。最初，通过探索性数据分析对图像进行图形表示，以研究像素强度，并通过提取RGB直方图中的颜色通道来发现异常。随后，使用图像增强和对比度增强技术对数据集进行预处理，以去除噪声信号。此外，还应用了诸如轮廓特征的形态值和大津阈值处理等特征提取技术来提取特征。根据各种参数对模型进行了评估，发现在测试阶段，InceptionResNetV2模型的准确率最高，为88%，最佳损失值为0.399，均方根误差为0.63。
