Maneerat, Noppadol

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Maneerat, Noppadol
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noppadol.ma@kmitl.ac.th

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    Item type:Publication,
    Brain Tumor Classification using Supervised Support Vector Machine
    (2023-01-01)
    Pilaoon, Pongsak
    ;
    Maneerat, Noppadol
    ;
    Yajima, Kuniaki
    ;
    Varakulsiripunth, Ruttikorn
    ;
    Hamamoto, Kazuhiko
    A Glioblastoma (GBM) is a malignant brain tumor earlier detection and diagnosis will increase survival opportunities. This research has developed for binary classification of GBM brain tumors by supervised machine learning from magnetic resonance imaging (MRI). DICOM medical images have been converted into JPEG files and morphological operation has been implemented to separate the brain region from the skull image for preparation and easier for tumor segmentation in preprocessing stage. The global thresholding segmentation has been proposed to segment the brain tumor from the artifact and then the features have been extracted by gray level coefficient matrix feature extraction (GLCM). In this research, a support vector machine has been conducted for binary classification and finally, GBM grade-4 brain tumor is distinguished from normal brain images. The dataset comprises 155 MRI images 80% has been assigned for training and another 20% will be the testing dataset. The experimental output prediction result is 96.875 % accuracy, 95 % sensitivity, and 100% specificity. The performance of classification has been improved and shown better results when compared with previous research work.
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    Brain Tumor Classification using Pretrained Deep Convolutional Neural Network
    (2023-01-01)
    Pilaoon, Pongsak
    ;
    Maneerat, Noppadol
    ;
    Nakthewan, Athasart
    ;
    Varakulsiripunth, Ruttikorn
    ;
    Hamamoto, Kazuhiko
    In this research deep learning convolution neural network (CNN) has been implemented for binary classification GBM brain tumor. The dataset from REMBRANDT database that comprise of 155 MRI images has been utilized in this research. The transfer learning by pretrained network namely GoogleNet and AlexNet have been conducted to classify the GBM brain tumor form normal brain. The advantage of classification by transfer learning is the manual segmentation and feature extraction were replaced by automatic procedure that reduce the error from human handcraft. The prediction result by using Googlenet pretrained network shown accuracy result 80.85% and Alexnet pretrained network obtained accuracy 93.62%. The GBM brain tumor classification by deep learning pretrained network obtained good result of accuracy and can be implemented in practical to help the medical staff for earlier diagnosis for further treatment and increasing survivor rate of patients. The manual adjustment for segmentation and feature extraction are improved by automatic classification using deep learning pretrained network is main advantage of this research. The future work we will try to implement with increasing images dataset to improve accuracy, robustness testing and prevent overfitting problem.