| Yazarlar (2) |
Arş. Gör. Makbule Hilal MÜTEVELLİ ÖNCÜL
Kastamonu Üniversitesi, Türkiye |
Semih Ergin
Eskişehir Osmangazi Üniversitesi, Türkiye |
| Özet |
| Accurate and automatic detections of brain tumors are vital. The aim of this study is to detect brain tumors in Magnetic Resonance (MR) images and to classify these tumors with a high degree of accuracy. After removing skull, the suspicious regions including tumors in the MR images were detected by using K-means clustering, K-means clustering in Lab color space, and the Chan-Vese without edges algorithm. At this stage, a performance evaluation of these three different methods was investigated, and it was seen that the best result was obtained in the Chan-Vese active contour without edges algorithm. For the classification stage, various features such as shape-based features, gray level co-occurrence matrix features, histogram of oriented gradients features, local binary pattern features, and statistical features were extracted from the detected suspicious regions. Finally, the suspicious regions were classified by k-nearest neighbor (k-NN), Fisher's linear discriminant analysis (FLDA), random forest, decision tree, support vector machines (SVM), logistic linear classifier (LLC), and Naive Bayes classification methods. As a result of this study, it was determined that the FLDA classifier provided the best results with 93.01% accuracy, 93.46% sensitivity, and 96.50% specificity rates in classification for benign tumors, malignant tumors, and healthy (without tumor) cases. |
| Anahtar Kelimeler |
| Brain tumor | Computer aided detection | Skull removal | Suspicious region detection |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | TRAITEMENT DU SIGNAL |
| Dergi ISSN | 0765-0019 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | Türkçe |
| Basım Tarihi | 08-2021 |
| Cilt No | 38 |
| Sayı | 4 |
| Sayfalar | 967 / 978 |
| Doi Numarası | 10.18280/ts.380406 |
| Makale Linki | http://dx.doi.org/10.18280/ts.380406 |
| Atıf Sayıları | |
| WoS | 1 |
| SCOPUS | 1 |
