Division of Research and Innovation, Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India.
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
Front Public Health. 2023 Jan 30;11:1109236. doi: 10.3389/fpubh.2023.1109236. eCollection 2023.
Cancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management.
The proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation.
This experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.
由于多种原因，人类的癌症发病率正在逐渐上升，因此，明智的检测和管理对于降低发病率至关重要。肾脏是人体生理学中的重要器官之一，肾脏癌症是一种医疗急症，需要准确的诊断和妥善的管理。
本研究旨在开发一种使用预训练的深度学习方案将肾脏计算机断层扫描（CT）图像分类为健康/癌症类别的框架。为了提高检测准确性，本工作提出了一种基于阈值滤波器的预处理方案，有助于去除 CT 切片中的伪影，以实现更好的检测。该方案的各个阶段包括：（i）图像采集、调整大小和去除伪影，（ii）深度特征提取，（iii）特征减少和融合，以及（iv）使用五重交叉验证进行二进制分类。
本实验分别针对：（i）有伪影的 CT 切片和（ii）无伪影的 CT 切片进行了研究。作为本研究实验结果的一部分，K-最近邻（KNN）分类器能够通过使用预处理的 CT 切片实现 100%的检测准确性。因此，该方案可用于检查临床级别的肾脏 CT 图像，因为它具有重要的临床意义。
