Niranjan K, Shankar Kumar S, Vedanth S, Chitrakala Dr S
Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India.
Procedia Comput Sci. 2023;218:1915-1925. doi: 10.1016/j.procs.2023.01.168. Epub 2023 Jan 31.
The coronavirus has caused havoc on billions of people worldwide. The Reverse Transcription Polymerase Chain Reaction(RT-PCR) test is widely accepted as a standard diagnostic tool for detecting infection, however, the severity of infection can't be measured accurately with RT-PCR results. Chest CT Scans of infected patients can manifest the presence of lesions with high sensitivity. During the pandemic, there is a dearth of competent doctors to examine chest CT images. Therefore, a Guided Gradcam based Explainable Classification and Segmentation system (GGECS) which is a real-time explainable classification and lesion identification decision support system is proposed in this work. The classification model used in the proposed GGECS system is inspired by Res2Net. Explainable AI techniques like GradCam and Guided GradCam are used to demystify Convolutional Neural Networks (CNNs). These explainable systems can assist in localizing the regions in the CT scan that contribute significantly to the system's prediction. The segmentation model can further reliably localize infected regions. The segmentation model is a fusion between the VGG-16 and the classification network. The proposed classification model in GGECS obtains an overall accuracy of 98.51 % and the segmentation model achieves an IoU score of 0.595.
新冠病毒给全球数十亿人带来了灾难。逆转录聚合酶链反应（RT-PCR）检测被广泛认可为检测感染的标准诊断工具，然而，无法通过RT-PCR结果准确衡量感染的严重程度。感染患者的胸部CT扫描能够高灵敏度地显示病变的存在。在疫情期间，缺乏专业医生来检查胸部CT图像。因此，本文提出了一种基于引导式梯度加权类激活映射的可解释分类与分割系统（GGECS），这是一个实时的可解释分类和病变识别决策支持系统。所提出的GGECS系统中使用的分类模型受到Res2Net的启发。诸如梯度加权类激活映射（GradCam）和引导式梯度加权类激活映射（Guided GradCam）等可解释人工智能技术被用于揭开卷积神经网络（CNN）的神秘面纱。这些可解释系统可以帮助定位CT扫描中对系统预测有重大贡献的区域。分割模型可以进一步可靠地定位感染区域。分割模型是VGG-16和分类网络的融合。GGECS中提出的分类模型总体准确率达到98.51%，分割模型的交并比分数达到0.595。
