Ghosh Susmita, Das Swagatam, Mallipeddi Rammohan
Electronics and Communication Sciences UnitIndian Statistical Institute Kolkata 700108 India.
Department of Artificial IntelligenceKyungpook National University Daegu 7027021 South Korea.
IEEE Access. 2021 Dec 6;9:163686-163696. doi: 10.1109/ACCESS.2021.3133338. eCollection 2021.
The development of a computer-aided disease detection system to ease the long and arduous manual diagnostic process is an emerging research interest. Living through the recent outbreak of the COVID-19 virus, we propose a machine learning and computer vision algorithms-based automatic diagnostic solution for detecting the COVID-19 infection. Our proposed method applies to chest radiograph that uses readily available infrastructure. No studies in this direction have considered the spatial aspect of the medical images. This motivates us to investigate the role of spectral-domain information of medical images along with the spatial content towards improved disease detection ability. Successful integration of spatial and spectral features is demonstrated on the COVID-19 infection detection task. Our proposed method comprises three stages - Feature extraction, Dimensionality reduction via projection, and prediction. At first, images are transformed into spectral and spatio-spectral domains by using Discrete cosine transform (DCT) and Discrete Wavelet transform (DWT), two powerful image processing algorithms. Next, features from spatial, spectral, and spatio-spectral domains are projected into a lower dimension through the Convolutional Neural Network (CNN), and those three types of projected features are then fed to Multilayer Perceptron (MLP) for final prediction. The combination of the three types of features yielded superior performance than any of the features when used individually. This indicates the presence of complementary information in the spectral domain of the chest radiograph to characterize the considered medical condition. Moreover, saliency maps corresponding to classes representing different medical conditions demonstrate the reliability of the proposed method. The study is further extended to identify different medical conditions using diverse medical image datasets and shows the efficiency of leveraging the combined features. Altogether, the proposed method exhibits potential as a generalized and robust medical image-assisted diagnostic solution.
开发一种计算机辅助疾病检测系统以简化漫长而艰巨的手动诊断过程是一个新兴的研究热点。经历了最近的新冠病毒疫情，我们提出了一种基于机器学习和计算机视觉算法的自动诊断解决方案，用于检测新冠病毒感染。我们提出的方法适用于使用现成基础设施的胸部X光片。此前尚无该方向的研究考虑医学图像的空间方面。这促使我们研究医学图像的光谱域信息以及空间内容在提高疾病检测能力方面的作用。在新冠病毒感染检测任务中证明了空间和光谱特征的成功整合。我们提出的方法包括三个阶段——特征提取、通过投影进行降维以及预测。首先，使用离散余弦变换（DCT）和离散小波变换（DWT）这两种强大的图像处理算法将图像转换到光谱域和时空光谱域。接下来，通过卷积神经网络（CNN）将来自空间、光谱和时空光谱域的特征投影到更低维度，然后将这三种类型的投影特征输入到多层感知器（MLP）进行最终预测。三种类型特征的组合产生了比单独使用任何一种特征都更优的性能。这表明胸部X光片的光谱域中存在互补信息来表征所考虑的医学状况。此外，对应于代表不同医学状况的类别的显著性图证明了所提方法的可靠性。该研究进一步扩展到使用不同的医学图像数据集来识别不同的医学状况，并展示了利用组合特征的效率。总之，所提方法展现出作为一种通用且强大的医学图像辅助诊断解决方案的潜力。
