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Kaya, I.E., Pehlivanl, A.A., Sekizkarde, E.G. and Ibrikci, T. (2017) PCA Based Clustering for Brain Tumor Segmentation of T1w MRI Images. Computer Methods and Programs in Biomedicine, 140, 19-28.
https://doi.org/10.1016/j.cmpb.2016.11.011

has been cited by the following article:

• TITLE: Detection and Classification of Brain Tumor Based on Multilevel Segmentation with Convolutional Neural Network

  AUTHORS: Rafiqul Islam, Shah Imran, Md. Ashikuzzaman, Md. Munim Ali Khan

  KEYWORDS: Segmentation, Classification, Non-Cancerous Tumor, Cancerous Tumor, Feature Extraction, Convolutional Neural Network

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.13 No.4, April 30, 2020

  ABSTRACT: Magnetic Resonance Imaging (MRI) is an important diagnostic technique for early detection of brain Tumor and the classification of brain Tumor from MRI image is a challenging research work because of its different shapes, location and image intensities. For successful classification, the segmentation method is required to separate Tumor. Then important features are extracted from the segmented Tumor that is used to classify the Tumor. In this work, an efficient multilevel segmentation method is developed combining optimal thresholding and watershed segmentation technique followed by a morphological operation to separate the Tumor. Convolutional Neural Network (CNN) is then applied for feature extraction and finally, the Kernel Support Vector Machine (KSVM) is utilized for resultant classification that is justified by our experimental evaluation. Experimental results show that the proposed method effectively detect and classify the Tumor as cancerous or non-cancerous with promising accuracy.