دانشگاه سمنان

 آمار

تعداد نشریات22
تعداد شماره‌ها709
تعداد مقالات10,213
تعداد مشاهده مقاله71,680,608
تعداد دریافت فایل اصل مقاله63,498,573
Hashemi, Seyyed Mohammad Reza, Hassanpour, Hamid, Kozegar, Ehsan, Tan, Tao. (1398). Cystoscopy Image Classification Using Deep Convolutional Neural Networks. هنرهای کاربردی, 10(1), 193-215. doi: 10.22075/ijnaa.2019.4064
Seyyed Mohammad Reza Hashemi; Hamid Hassanpour; Ehsan Kozegar; Tao Tan. "Cystoscopy Image Classification Using Deep Convolutional Neural Networks". هنرهای کاربردی, 10, 1, 1398, 193-215. doi: 10.22075/ijnaa.2019.4064
Hashemi, Seyyed Mohammad Reza, Hassanpour, Hamid, Kozegar, Ehsan, Tan, Tao. (1398). 'Cystoscopy Image Classification Using Deep Convolutional Neural Networks', هنرهای کاربردی, 10(1), pp. 193-215. doi: 10.22075/ijnaa.2019.4064
Hashemi, Seyyed Mohammad Reza, Hassanpour, Hamid, Kozegar, Ehsan, Tan, Tao. Cystoscopy Image Classification Using Deep Convolutional Neural Networks. هنرهای کاربردی, 1398; 10(1): 193-215. doi: 10.22075/ijnaa.2019.4064

Cystoscopy Image Classification Using Deep Convolutional Neural Networks

International Journal of Nonlinear Analysis and Applications
مقاله 18، دوره 10، شماره 1، بهمن 2019، صفحه 193-215    اصل مقاله (2.35 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2019.4064
نویسندگان
Seyyed Mohammad Reza Hashemi1؛ Hamid Hassanpour1؛ Ehsan Kozegar* 2؛ Tao Tan3
1Faculty of Computer Engineering and IT, Shahrood University of Technology, Shahrood, Iran
2University of Guilan, Guilan, Iran
3Eindhoven University of Technology, Eindhoven, The Netherlands
تاریخ دریافت: 16 آذر 1396،  تاریخ بازنگری: 23 آبان 1397،  تاریخ پذیرش: 28 آبان 1397 
چکیده
In the past three decades, the use of smart methods in medical diagnostic systems has attracted the attention of many researchers. However, no smart activity has been provided in the field of medical image processing for diagnosis of bladder cancer through cystoscopy images despite the high prevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) and a multilayer neural network was applied to classify bladder cystoscopy images. One of the most important issues in training phase of neural networks is determining the learning rate because selecting too small or large learning rate leads to slow convergence, volatility and divergence, respectively. Therefore, an algorithm is required to dynamically change the convergence rate. In this respect, an adaptive method was presented for determining the learning rate so that the multilayer neural network could be improved. In this method, the learning rate is determined using a coefficient based on the difference between the accuracy of training and validation according to the output error. In addition, the rate of changes is updated according to the level of weight changes and output error. The proposed method was evaluated on 720 bladder cystoscopy images in four classes of blood in urine, benign and malignant masses. Based on the simulated results, the second proposed method (CNNs) achieved at least 17% decrease in error and increased the convergence speed of the proposed method in the classification of cystoscopy images, compared to the other competing methods.
کلیدواژه‌ها
Cystoscopy Images؛ Medical Image Classification؛ MLP Neural Network؛ Adaptive Learning Rate؛ CNNs
آمار
تعداد مشاهده مقاله: 16,278
تعداد دریافت فایل اصل مقاله: 9,453


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب