Abstract
Automatic classification of brain tumor types is very important for accelerating the treatment process, planning and increasing the patient’s survival rate. MR images are used to determine the type of brain tumor. Manual diagnosis of brain tumor type depends on the experience and sensitivity of radiologists. Therefore, researchers have developed many brain tumor classification models to minimize the human factor. In this study, two different feature extraction LGP and LBP approaches were used to classify the most common brain tumor types; Glioma and meningioma, Detection of a brain tumor is an essential process because of the difficulty in distinguishing between abnormal and normal tissues. With the right diagnosis, the patient can get excellent treatment, extending their lifespan. Despite all the research, there are still significant limitations in detecting tumor areas because of abnormal lesion distribution. It may be challenging to locate an area with very few tumor cells because areas with such small areas frequently appear healthy. Studies are becoming more common in which automated classification of early-stage brain tumors is performed using deep learning or machine learning approaches. This study proposes a hybrid deep learning model for the detection and early diagnosis of brain tumors via magnetic resonance imaging. The dataset images were subjected to a Local Binary Pattern (LBP).
Keywords: LBP, Brain tumor, Local feature Descriptor, Recognition of pixel, Feature extraction.
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Corresponding Author
Mr.S.Prakasha
Assistant Professor, Department of Electrical and Electronics Engineering, Proudhadevaraya Institute of Technology, Hosapete, Karnataka, India
DOI: https://dx.doi.org/10.18535/jmscr/v11i12.21
