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Computer-Aided Diagnosis (CAD) is in wide practice in clinical work for the detection and prognosis of various types of abnormalities. For this, medical images acquired in various tests by utilizing diverse imaging modalities are used. In medical imaging, detecting and classifying the brain tumors in Magnetic Resonance Image (MRI) is a demanding and critical task. MRI gives anatomical structure's information and the potential abnormal tissues' information. Thus, this paper proposes a new system for MRI brain tumor detection and classification for multi class tumor images. The proposed system comprises feature extraction and classification. In feature extraction, the attribute of the co-occurrence matrix and the histogram is represented within this feature vector. In classification, particle swam optimization neural network is trained and tested to perform automatic classification of four different types of brain tumor.The method was applied on a population of 102 brain tumors histologically diagnosed as Meningioma (115), Metastasis (120), Gliomas grade II (65) and Gliomas grade II (70). Classification accuracy of proposed system in class 1(Meningioma) type tumor is 98.6%, class 2 (Metastasis) is 99.29%, class 3(Gliomas grade II) is 97.87 and class 4(Gliomas grade III) is 98.6%.

revista técnica de la facultad de ingeniería. universidad del zulia/revista técnica de la facultad de ingeniería

Computer-Aided Diagnosis System for Classification of Multi- Class Brain Tumor in MRI Images using Hybrid Texton Structure Descriptor and Particle Swam Optimized Neural Network