Histopathology image analysis for gastric cancer detection: a hybrid deep learning and catboost approach

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Danial Khayatian, Alireza Maleki, Hamid Nasiri, Morteza Dorrigiv
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引用次数: 0

Abstract

Since gastric cancer is growing fast, accurate and prompt diagnosis is essential, utilizing computer-aided diagnosis (CAD) systems is an efficient way to achieve this goal. Using methods related to computer vision enables more accurate predictions and faster diagnosis, leading to timely treatment. CAD systems can categorize photos effectively using deep learning techniques based on image analysis and classification. Accurate and timely classification of histopathology images is critical for enabling immediate treatment strategies, but remains challenging. We propose a hybrid deep learning and gradient-boosting approach that achieves high accuracy in classifying gastric histopathology images. This approach examines two classifiers for six networks known as pre-trained models to extract features. Extracted features will be fed to the classifiers separately. The inputs are gastric histopathological images. The GasHisSDB dataset provides these inputs containing histopathology gastric images in three 80px, 120px, and 160px cropping sizes. According to these achievements and experiments, we proposed the final method, which combines the EfficientNetV2B0 model to extract features from the images and then classify them using the CatBoost classifier. The results based on the accuracy score are 89.7%, 93.1%, and 93.9% in 80px, 120px, and 160px cropping sizes, respectively. Additional metrics including precision, recall, and F1-scores were above 0.9, demonstrating strong performance across various evaluation criteria. In another way, to approve and see the model efficiency, the GradCAM algorithm was implemented. Visualization via Grad-CAM illustrated discriminative regions identified by the model, confirming focused learning on histologically relevant features. The consistent accuracy and reliable detections across diverse evaluation metrics substantiate the robustness of the proposed deep learning and gradient-boosting approach for gastric cancer screening from histopathology images. For this purpose, two types of outputs (The heat map and the GradCAM output) are provided. Additionally, t-SNE visualization showed a clear clustering of normal and abnormal cases after EfficientNetV2B0 feature extraction. The cross-validation and visualizations provide further evidence of generalizability and focused learning of meaningful pathology features for gastric cancer screening from histopathology images.

Abstract Image

用于胃癌检测的组织病理学图像分析:深度学习和 catboost 混合方法
由于胃癌发展迅速,准确和及时的诊断至关重要,利用计算机辅助诊断(CAD)系统是实现这一目标的有效途径。使用与计算机视觉相关的方法可以实现更准确的预测和更快速的诊断,从而获得及时的治疗。计算机辅助诊断系统可以利用基于图像分析和分类的深度学习技术对照片进行有效分类。准确、及时地对组织病理学图像进行分类对于制定即时治疗策略至关重要,但这仍然具有挑战性。我们提出了一种混合深度学习和梯度提升方法,可在胃组织病理学图像分类中实现高准确度。这种方法为六个网络(称为预训练模型)检查了两个分类器,以提取特征。提取的特征将分别输入分类器。输入是胃组织病理学图像。GasHisSDB 数据集提供了这些输入,其中包含 80px、120px 和 160px 三种裁剪尺寸的胃组织病理学图像。根据这些成果和实验,我们提出了最终方法,即结合 EfficientNetV2B0 模型从图像中提取特征,然后使用 CatBoost 分类器进行分类。在 80px、120px 和 160px 裁剪尺寸下,基于准确率得分的结果分别为 89.7%、93.1% 和 93.9%。其他指标,包括精确度、召回率和 F1 分数均高于 0.9,显示了在各种评估标准中的优异表现。另外,为了验证和观察模型的效率,我们采用了 GradCAM 算法。通过 Grad-CAM 的可视化显示了模型识别出的鉴别区域,证实了对组织学相关特征的集中学习。在不同的评估指标中,一致的准确性和可靠的检测结果证明了所提出的深度学习和梯度提升方法在组织病理学图像胃癌筛查中的稳健性。为此,我们提供了两种类型的输出(热图和 GradCAM 输出)。此外,经过 EfficientNetV2B0 特征提取后,t-SNE 可视化显示了正常和异常病例的清晰聚类。交叉验证和可视化进一步证明了从组织病理学图像中筛选胃癌的通用性和有意义病理学特征的集中学习。
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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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