Department of Computer Science and Engineering, Siksha O Anusandhan (Deemed to Be University), Bhubaneswar, 751030, India.
Department of Computer Science, Aditya Institute of Technology and Management, Srikakulam, Andhra Pradesh, 532201, India.
Interdiscip Sci. 2021 Jun;13(2):212-228. doi: 10.1007/s12539-021-00417-8. Epub 2021 Feb 10.
This work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time-frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for training and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coefficients. Each class of decomposed images is later given as input to a couple of considered convolutional neural network (CNN) models for training and testing in two different classification levels to recognize its predicted value. Afterward, the classification performance is measured through the following measuring indices: accuracy, precision, recall, specificity, and F1 score. The experimental result shows 97.25% and 93.75% of accuracy in the first level and second level of classification, respectively. Lastly, a comparative performance analysis is carried out with several other previously published works on a similar dataset where the proposed approach performs better than its contemporary methods.
这项工作提出了一种智能医疗保健系统，用于通过时频分析和卷积神经网络检测胃肠道（GI）区域中存在的各种异常。在这方面，使用了包含八个 GI 道图像类别的 KVASIR V2 数据集，例如正常盲肠、正常幽门、正常 Z 线、食管炎、息肉、溃疡性结肠炎、染色和提起的息肉以及染色切除边缘，用于训练和验证。这项工作的初始阶段涉及图像预处理步骤，然后提取近似离散小波变换系数。后来，将分解图像的每类作为输入提供给几个考虑的卷积神经网络（CNN）模型，以便在两个不同的分类级别进行训练和测试，以识别其预测值。之后，通过以下测量指标来衡量分类性能：准确性、精度、召回率、特异性和 F1 分数。实验结果表明，在第一级和第二级分类中，准确性分别为 97.25%和 93.75%。最后，在类似的数据集上与其他几个先前发表的工作进行了比较性能分析，其中提出的方法优于其当代方法。
