Ali Ahmed, Iqbal Arshad, Khan Sohail, Ahmad Naveed, Shah Sajid
School of Computing Sciences, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang, Haripur, Khyber Pakhtunkhwa, Pakistan.
Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule Institute of Applied Sciences and Technology, Mang, Haripur, Khyber Pakhtunkhwa, Pakistan.
PeerJ Comput Sci. 2024 Dec 19;10:e2587. doi: 10.7717/peerj-cs.2587. eCollection 2024.
Gastrointestinal (GI) disorders are common and often debilitating health issues that affect a significant portion of the population. Recent advancements in artificial intelligence, particularly computer vision algorithms, have shown great potential in detecting and classifying medical images. These algorithms utilize deep convolutional neural network architectures to learn complex spatial features in images and make predictions for similar unseen images. The proposed study aims to assist gastroenterologists in making more efficient and accurate diagnoses of GI patients by utilizing its two-phase transfer learning framework to identify GI diseases from endoscopic images. Three pre-trained image classification models, namely Xception, InceptionResNetV2, and VGG16, are fine-tuned on publicly available datasets of annotated endoscopic images of the GI tract. Additionally, two custom convolutional neural networks are constructed and fully trained for comparative analysis of their performance. Four different classification tasks are examined based on the endoscopic image categories. The proposed architecture employing InceptionResNetV2 achieves the most consistent and generalized performance across most classification tasks, yielding accuracy scores of 85.7% for general classification of GI tract (eight-category classification), 97.6% for three-diseases classification, 99.5% for polyp identification (binary classification), and 74.2% for binary classification of esophagitis severity on unseen endoscopic images. The results indicate the effectiveness of the two-phase transfer learning framework for clinical use to enhance the identification of GI diseases, aiding in their early diagnosis and treatment.
胃肠道（GI）疾病是常见且往往使人衰弱的健康问题，影响着相当一部分人口。人工智能领域的最新进展，特别是计算机视觉算法，在医学图像检测和分类方面显示出巨大潜力。这些算法利用深度卷积神经网络架构来学习图像中的复杂空间特征，并对类似的未见图像进行预测。本研究旨在通过利用其两阶段迁移学习框架从内镜图像中识别胃肠道疾病，协助胃肠病学家对胃肠道疾病患者做出更高效、准确的诊断。在公开可用的胃肠道注释内镜图像数据集上对三个预训练的图像分类模型，即Xception、InceptionResNetV2和VGG16进行微调。此外，构建并完全训练了两个自定义卷积神经网络，以对其性能进行比较分析。根据内镜图像类别检查了四种不同的分类任务。所提出的采用InceptionResNetV2的架构在大多数分类任务中实现了最一致和通用的性能，在未见内镜图像上，胃肠道一般分类（八类分类）的准确率为85.7%，三种疾病分类的准确率为97.6%，息肉识别（二元分类）的准确率为99.5%，食管炎严重程度二元分类的准确率为74.2%。结果表明，两阶段迁移学习框架在临床应用中对于增强胃肠道疾病的识别是有效的，有助于其早期诊断和治疗。
