Doğan Refika Sultan, Yılmaz Bülent
Department of Bioengineering, Abdullah Gül University, Kayseri, Türkiye.
Biomedical Instrumentation and Signal Analysis Laboratory, Abdullah Gül University, Kayseri, Türkiye.
Front Oncol. 2024 Jan 17;13:1325271. doi: 10.3389/fonc.2023.1325271. eCollection 2023.
The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial intelligence algorithms, such as convolutional neural networks, have shown remarkable capabilities in pathology image analysis tasks, including tumor identification, metastasis detection, and patient prognosis assessment. However, traditional manual analysis methods have generally shown low accuracy in diagnosing colorectal cancer using histopathological images. This study investigates the use of AI in image classification and image analytics using histopathological images using the histogram of oriented gradients method. The study develops an AI-based architecture for image classification using histopathological images, aiming to achieve high performance with less complexity through specific parameters and layers. In this study, we investigate the complicated state of histopathological image classification, explicitly focusing on categorizing nine distinct tissue types. Our research used open-source multi-centered image datasets that included records of 100.000 non-overlapping images from 86 patients for training and 7180 non-overlapping images from 50 patients for testing. The study compares two distinct approaches, training artificial intelligence-based algorithms and manual machine learning models, to automate tissue classification. This research comprises two primary classification tasks: binary classification, distinguishing between normal and tumor tissues, and multi-classification, encompassing nine tissue types, including adipose, background, debris, stroma, lymphocytes, mucus, smooth muscle, normal colon mucosa, and tumor. Our findings show that artificial intelligence-based systems can achieve 0.91 and 0.97 accuracy in binary and multi-class classifications. In comparison, the histogram of directed gradient features and the Random Forest classifier achieved accuracy rates of 0.75 and 0.44 in binary and multi-class classifications, respectively. Our artificial intelligence-based methods are generalizable, allowing them to be integrated into histopathology diagnostics procedures and improve diagnostic accuracy and efficiency. The CNN model outperforms existing machine learning techniques, demonstrating its potential to improve the precision and effectiveness of histopathology image analysis. This research emphasizes the importance of maintaining data consistency and applying normalization methods during the data preparation stage for analysis. It particularly highlights the potential of artificial intelligence to assess histopathological images.
随着数字病理学的出现，组织病理学图像分析领域有了显著发展，催生了能够对各种病理图像中的组织和结构进行分类的自动化模型。诸如卷积神经网络等人工智能算法在病理图像分析任务中展现出卓越能力，包括肿瘤识别、转移检测以及患者预后评估。然而，传统的手工分析方法在使用组织病理学图像诊断结直肠癌时，总体准确率较低。本研究使用定向梯度直方图方法，探讨人工智能在组织病理学图像分类和图像分析中的应用。该研究开发了一种基于人工智能的使用组织病理学图像进行图像分类的架构，旨在通过特定参数和层实现高性能且复杂度更低。在本研究中，我们探究了组织病理学图像分类的复杂状况，特别聚焦于对九种不同组织类型进行分类。我们的研究使用了开源的多中心图像数据集，其中包括来自86名患者的100000张非重叠图像记录用于训练，以及来自50名患者的7180张非重叠图像用于测试。该研究比较了两种不同的方法，即训练基于人工智能的算法和手工机器学习模型，以实现组织分类自动化。本研究包含两项主要分类任务：二元分类，区分正常组织和肿瘤组织；多分类，涵盖九种组织类型，包括脂肪、背景、碎片、基质、淋巴细胞、黏液、平滑肌、正常结肠黏膜和肿瘤。我们的研究结果表明，基于人工智能的系统在二元分类和多分类中分别能达到0.91和0.97的准确率。相比之下，定向梯度特征直方图和随机森林分类器在二元分类和多分类中的准确率分别为0.75和0.44。我们基于人工智能的方法具有通用性，能够融入组织病理学诊断程序，提高诊断准确性和效率。卷积神经网络模型优于现有的机器学习技术，证明了其在提高组织病理学图像分析的精度和有效性方面的潜力。本研究强调了在数据准备阶段进行分析时保持数据一致性和应用归一化方法的重要性。它特别突出了人工智能在评估组织病理学图像方面的潜力。
