College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India.
Sensors (Basel). 2022 May 27;22(11):4079. doi: 10.3390/s22114079.
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.
每年，近 200 万人因胃肠道（GI）疾病而死亡。下消化道肿瘤是全球范围内导致死亡的主要原因之一。因此，早期发现肿瘤类型对于患者的生存至关重要。此外，早期切除良性肿瘤的风险大于收益。视频内窥镜技术是对胃肠道进行成像和识别出血、溃疡、息肉和恶性肿瘤等疾病的重要手段。摄像会生成 5000 帧图像，需要进行广泛的分析，并需要很长时间来跟踪所有的帧。因此，具有更高诊断能力并能帮助医生做出准确诊断决策的人工智能技术解决了这些挑战。在这项研究中，开发了许多多方法，其中工作分为四个提出的系统；每个系统都有超过一种诊断方法。第一个提出的系统利用人工神经网络（ANN）和前馈神经网络（FFNN）算法，通过三种算法提取混合特征：局部二值模式（LBP）、灰度共生矩阵（GLCM）和模糊颜色直方图（FCH）算法。第二个提出的系统使用预先训练的 CNN 模型，即基于提取深度特征图及其高精度分类的 GoogLeNet 和 AlexNet。第三个提出的方法使用混合技术，由两个块组成：第一个块是 CNN 模型（GoogLeNet 和 AlexNet），用于提取特征图；第二个块是支持向量机（SVM）算法，用于分类深度特征图。第四个提出的系统基于 CNN 模型（GoogLeNet 和 AlexNet）和 LBP、GLCM 和 FCH 算法之间的混合特征，使用 ANN 和 FFNN。所有提出的系统在诊断内窥镜图像以早期发现下消化道疾病方面都取得了优异的结果。所有系统都产生了有希望的结果；基于 GoogLeNet、LBP、GLCM 和 FCH 提取的混合特征的 FFNN 分类器达到了 99.3%的准确率、99.2%的精度、99%的灵敏度、100%的特异性和 99.87%的 AUC。
