Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea.
J Med Internet Res. 2020 Nov 26;22(11):e18563. doi: 10.2196/18563.
The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning-based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract.
This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases.
Our proposed framework comprises a deep learning-based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment.
All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods.
This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.
各种胃肠道疾病的早期诊断可以实现有效的治疗并降低许多危及生命的情况的风险。不幸的是，在医学专家进行的早期检查中，各种小的胃肠道病变无法被检测到。在之前的研究中，各种基于深度学习的计算机辅助诊断工具已经为胃肠道疾病的有效诊断和治疗做出了重大贡献。然而，这些方法大多旨在检测特定人类胃肠道部位的有限数量的胃肠道疾病，如息肉、肿瘤或癌症。
本研究旨在开发一种全面的计算机辅助诊断工具，以协助医学专家诊断各种类型的胃肠道疾病。
我们提出的框架包括基于深度学习的分类网络和检索方法。在第一步中，分类网络预测当前医疗状况的疾病类型。然后，框架的检索部分显示来自以前数据库的相关病例（内窥镜图像）。这些过去的病例有助于医学专家主观验证当前计算机预测，从而最终实现更好的诊断和治疗。
所有实验均使用 2 个内窥镜数据集进行，共有 52471 个帧和 37 个不同类别。我们提出的方法在准确性、F1 分数、平均准确率和平均召回率方面获得的最佳性能分别为 96.19%、96.99%、98.18%和 95.86%。我们提出的诊断框架的整体性能明显优于最先进的方法。
本研究提供了一种用于识别各种类型胃肠道疾病的全面计算机辅助诊断框架。结果表明，我们提出的方法优于各种其他最新方法，并说明了其在临床诊断和治疗中的潜力。我们提出的网络可应用于医学成像中的其他分类领域，如计算机断层扫描、磁共振成像和超声序列。
