Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India.
Med Biol Eng Comput. 2022 Sep;60(9):2681-2691. doi: 10.1007/s11517-022-02632-x. Epub 2022 Jul 14.
Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.
深度学习为医疗保健行业提供了以卓越的速度分析数据的能力，而不会牺牲准确性。这些技术可应用于医疗保健领域，以实现准确和及时的预测。卷积神经网络是一类深度学习方法，已在各种计算机视觉任务中占据主导地位，并在包括放射学在内的各种领域引起关注。由于结核病 (TB)、细菌性和病毒性肺炎以及 COVID-19 等肺部疾病的样本非常少，因此这些疾病的预测并不准确。这些疾病可以很容易地通过 X 射线或 CT 扫描图像来诊断。但是，每种疾病的可用图像数量并不相同，导致输入数据的不平衡性质。当使用较少的 COVID-19 数据样本进行训练时，传统的监督机器学习方法无法达到更高的准确性。图像数据增强是一种可以用来通过创建数据集图像的修改版本来人为地扩展训练数据集大小的技术。数据增强有助于减少深度神经网络训练时的过拟合。SMOTE（Synthetic Minority Oversampling Technique）算法用于平衡类。本研究工作的新颖之处在于在分类结核病、肺炎和 COVID-19 之前应用联合数据增强和类别平衡技术。在对模型进行训练后，使用所提出的多级分类获得的分类准确率记录为 TB 和肺炎为 97.4%，细菌、病毒和 COVID-19 分类为 88%。与该研究领域的现有方法相比，所提出的多级分类方法的分类准确率提高了约 8%至 10%。结果表明，所提出的系统可扩展到不断增长的医疗数据，并以更高的准确性在更短的时间内对肺部疾病及其亚型进行分类。
