Impact of activation functions and number of layers on detection of exudates using circular Hough transform and convolutional neural networks
| dc.contributor.author | Adem, Kemal | |
| dc.date.accessioned | 2022-06-23T06:18:07Z | |
| dc.date.available | 2022-06-23T06:18:07Z | |
| dc.date.issued | 2022 | |
| dc.department | Mühendislik Fakültesi | |
| dc.description.abstract | Convolutional neural networks (CNN), which are used for object detection, are widely used in image recognition and segmentation fields due to their good performance. In this study, a method is proposed for the detection of exudate from DR lesions based on image processing and CNN model with different activation functions and layer numbers. Activation functions are one of the main reasons why the CNN model is complicated in hierarchical structure. CNN models have real artificial intelligence capabilities, especially thanks to non-linear activation functions. Although ReLU is the most commonly used activation function in CNN models, it has some shortcomings in practice. The most important shortcoming is that if the input value is negative, the learning process will slow down due to the inability to take the derivative. In order to solve this problem, the effect of the activation function in the CNN model on a real world problem and its image was investigated in this study. By applying circular Hough transform, one of the image processing methods, the OD region, which has a similar structure to the exuding regions, was determined and removed from the image, and then the detection of the exuding regions was carried out using the CNN model. Exudate detection was performed using ReLU, ELU, Leaky ReLU, Softplus and Swish activation functions in the CNN model and the results were compared. Experimental results in DiaretDB0 and DiaretDB1 databases show that the Swish activation function has a better performance than the others. | |
| dc.identifier.doi | 10.1016/j.eswa.2022.117583 | |
| dc.identifier.endpage | - | en_US |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.issn | 1873-6793 | |
| dc.identifier.issue | - | en_US |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | - | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.1016/j.eswa.2022.117583 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/9469 | |
| dc.identifier.volume | 203 | en_US |
| dc.identifier.wos | WOS:000803570800006 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.relation.ispartof | Expert Systems with Applications | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Exudate | |
| dc.subject | Image Processing | |
| dc.subject | CNN | |
| dc.subject | Activation Functions | |
| dc.subject | ReLU | |
| dc.subject | Swish | |
| dc.title | Impact of activation functions and number of layers on detection of exudates using circular Hough transform and convolutional neural networks | |
| dc.type | Article |
