Sunnetci Kubilay Muhammed, Oguz Faruk Enes, Ekersular Mahmut Nedim, Gulenc Nadide Gulsah, Ozturk Mahmut, Alkan Ahmet
Department of Electrical and Electronics Engineering, Osmaniye Korkut Ata University, Osmaniye, Turkey.
Department of Electrical and Electronics Engineering, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Turkey.
J Imaging Inform Med. 2025 Jun;38(3):1722-1733. doi: 10.1007/s10278-024-01228-1. Epub 2024 Oct 25.
Bladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is crucial. Herein, endoscopic images are meticulously evaluated by experts, and sometimes even by different disciplines, to identify tissue types. It is believed that the time spent by experts can be utilized for patient treatment with the creation of a computer-aided decision support system. For this purpose, in this study, it is evaluated that the performances of three models proposed using the bladder tissue dataset. The first model is a convolutional neural network (CNN)-based deep learning (DL) network, and the second is a model named hybrid cnn-machine learning (ML) or DL + ML, which involves classifying deep features obtained from a CNN-based network with ML. The last one, and the one that achieved the best performance metrics, is a vision transformer (ViT) architecture. Furthermore, a graphical user interface (GUI) is provided for an accessible decision support system. As a result, accuracy and F1 score values for DL, DL + ML, and ViT models are 0.9086-0.8971-0.9257 and 0.8884-0.8496-0.8931, respectively.
膀胱癌在早期通常没有症状，是一种早期检测至关重要的癌症类型。在此，专家们，有时甚至是不同学科的专家，会仔细评估内镜图像以识别组织类型。人们认为，通过创建计算机辅助决策支持系统，专家花费的时间可用于患者治疗。为此，在本研究中，对使用膀胱组织数据集提出的三种模型的性能进行了评估。第一个模型是基于卷积神经网络（CNN）的深度学习（DL）网络，第二个是名为混合CNN-机器学习（ML）或DL+ML的模型，它涉及使用ML对从基于CNN的网络获得的深度特征进行分类。最后一个，也是性能指标最佳的模型，是视觉Transformer（ViT）架构。此外，还提供了一个图形用户界面（GUI）用于构建一个易于使用的决策支持系统。结果，DL、DL+ML和ViT模型的准确率和F1分数值分别为0.9086 - 0.8971 - 0.9257和0.8884 - 0.8496 - 0.8931。
