Name
Title | ||
|---|---|---|
Segmentation of precursor lesions in cervical cancer using convolutional neural networks. |
Abstract | ||
|---|---|---|
Cervical carcinoma is one of the frequently seen cancers in the world and in our country, develops from precursor lesions. These precursor lesions are analyzed by pathologists so that the diagnosis of the disease can be made. In this study, a system that performs automatic detection of pre-cancerous lesions was performed using the convolutional neural networks (CNNs). In the training phase, lesion recognition performance of the proposed system has reached 92%. Thereafter, whole image was segmented by using 60 x 60 pixel tiles during the training phase. After all, the precursor lesions were segmented with 81.71% Dice coefficient. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | Cervical cancer,histopathological images,precursor lesions,convolutional neural networks,segmentation |
Field | DocType | ISSN |
Computer vision,Lesion,Pattern recognition,Sørensen–Dice coefficient,Medical imaging,Segmentation,Convolutional neural network,Computer science,Image segmentation,Pixel,Artificial intelligence,Artificial neural network | Conference | 2165-0608 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdulkadir Albayrak | 1 | 6 | 2.86 |
| Asli Unlu | 2 | 1 | 0.36 |
| Calik, Nurullah | 3 | 2 | 2.42 |
| Gökhan Bilgin | 4 | 62 | 13.18 |
| Ilknur Turkmen | 5 | 1 | 1.38 |
| Asli Cakir | 6 | 1 | 0.70 |
| Abdulkerim Çapar | 7 | 19 | 4.28 |
| Behcet Ugur Töreyin | 8 | 1 | 1.72 |
| Lutfiye Durak-Ata | 9 | 46 | 13.89 |