Mahmood Saqib, Fareed Mian Muhammad Sadiq, Ahmed Gulnaz, Dawood Farhan, Zikria Shahid, Mostafa Ahmad, Jilani Syeda Fizzah, Asad Muhammad, Aslam Muhammad
Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
Department of Software Engineering, University of Central Punjab, Lahore 54000, Pakistan.
Biomedicines. 2022 Sep 5;10(9):2195. doi: 10.3390/biomedicines10092195.
Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics.
通过深度学习（DL）模型进行准确的患者疾病分类和检测，在生物医学成像领域的贡献越来越大。最常见的胃肠道（GI）疾病是消化性溃疡和胃癌。传统内窥镜检查对患者来说是一个痛苦且繁琐的过程，而无线胶囊内窥镜检查（WCE）是一种用于诊断胃肠道问题和进行无痛肠道成像的有用技术。然而，准确而高效地研究在WCE检查过程中捕获的数千张图像仍然是一个挑战，因为现有的深度模型在WCE图像分析上的准确率并不高。因此，为了预防患者出现紧急情况，我们需要一个高效且准确的DL模型用于实时分析。在本研究中，我们提出了一种可靠且高效的方法，通过应用深度卷积神经网络（CNN）对WCE图像中的胃肠道异常进行分类。为此，我们提出了一种名为胃肠道疾病检测网络（GIDD-Net）的自定义CNN架构，该架构是从零开始设计的，具有相对较少的参数，能够以较低的计算成本更准确、高效地检测胃肠道疾病。此外，我们的模型通过将胃碗中的类激活模式可视化为热图，成功地区分了胃肠道疾病。Kvasir-Capsule图像数据集存在严重的类别不平衡问题，我们采用了一种合成过采样技术——边界线合成少数类过采样技术（BL-SMOTE）来使图像在各类别之间均匀分布，以防止类别不平衡问题。所提出的模型根据各种指标进行评估，评估指标的取值如下：准确率为98.9%、AUC为99.8%、F1分数为98.9%、精确率为98.9%、召回率为98.8%，损失为0.0474。从模拟结果可以看出，所提出的模型在所有评估指标上均优于其他现有最先进的模型。
