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Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis.
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2019-11-13 , DOI: 10.1016/j.artmed.2019.101746
Ivan Lorencin 1 , Nikola Anđelić 1 , Josip Španjol 2 , Zlatan Car 1
Affiliation  

In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.



中文翻译:

使用带有Laplacian边缘检测器的多层感知器来诊断膀胱癌。

本文提出了一种基于多层感知器和拉普拉斯边缘检测器的膀胱癌诊断方法。本文的目的是研究一种更简单的方法(多层感知器)以及常用的方法(如深度学习卷积神经网络)以及用于膀胱癌检测的方法。用于这项研究的数据集包括1997年的膀胱癌图像和986张非癌组织图像。进行的研究结果表明,使用多层感知器对Laplacian边缘检测器预处理的图像进行训练和测试AUC值最高为0.99。当比较不同的图像尺寸时,可以看出,如果50×50100×100 使用图像。

更新日期:2019-11-13
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