Private Doğu Anadolu Hospital, Clinic of General Surgery, Elazig, Turkey.
Department of Pathology, Elazığ Fethi Sekin City Hospital, University of Health Sciences, Elazig, Turkey.
Breast Cancer Res Treat. 2023 Jul;200(2):183-192. doi: 10.1007/s10549-023-06970-8. Epub 2023 May 21.
Cancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The histo-pathological images are used to determine whether the tissue is cancerous and the type of cancer. As the result of examination on tissue images by the expert personnel, the cancer type, and stage of the tissue can be determined. However, this situation can cause both time and energy loss as well as personnel-related inspection errors. By the increased usage of computer-based decision methods in the last decades, it would be more efficient and accurate to detect and classify the cancerous tissues with computer-aided systems.
As classical image processing methods were used for cancer-type detection in early studies, advanced deep learning methods based on recurrent neural networks and convolutional neural networks have been used more recently. In this paper, popular deep learning methods such as ResNet-50, GoogLeNet, InceptionV3, and MobilNetV2 are employed by implementing novel feature selection method in order to classify cancer type on a local binary class dataset and multi-class BACH dataset.
The classification performance of the proposed feature selection implemented deep learning methods follows as for the local binary class dataset 98.89% and 92.17% for BACH dataset which is much better than most of the obtained results in literature.
The obtained findings on both datasets indicates that the proposed methods can detect and classify the cancerous type of a tissue with high accuracy and efficiency.
癌症是最阴险的疾病之一，克服癌症的最重要因素是早期诊断和检测。组织病理学图像用于确定组织是否癌变以及癌症的类型。专家人员通过对组织图像进行检查，可以确定组织的癌症类型和阶段。然而，这种情况可能会导致时间和精力的浪费以及人员相关的检查错误。在过去几十年中，随着基于计算机的决策方法的使用增加，使用计算机辅助系统更有效地检测和分类癌组织将变得更加准确。
在早期研究中，经典图像处理方法用于癌症类型检测，最近更多地使用基于递归神经网络和卷积神经网络的先进深度学习方法。在本文中，通过实施新颖的特征选择方法，使用了流行的深度学习方法，如 ResNet-50、GoogLeNet、InceptionV3 和 MobilNetV2，以便在局部二进制类数据集和多类 BACH 数据集上对癌症类型进行分类。
所提出的特征选择实现的深度学习方法的分类性能如下：对于局部二进制类数据集为 98.89%，对于 BACH 数据集为 92.17%，优于文献中获得的大多数结果。
在两个数据集上获得的结果表明，所提出的方法可以以高精度和高效率检测和分类组织的癌变类型。
