In recent times, Brain Tumors (BT) becomes the deadliest disease, which has increased the mortality rate over the globe. The optimal multilevel thresholding based segmentation and classification model is developed to diagnose BT. Initially, it performs preprocessing in three levels to enhance the quality of the image. Next, multi-level thresholding with artificial bee colony (ABC) algorithm is used for image segmentation. Afterward, the Gray Level Co-occurrence Matrix (GLCM) technique is employed as a feature extractor to extract a useful set of feature vectors. At last, the classification process takes place using a Support Vector Machine (SVM). A set of simulations were carried out using the benchmark Kaggle dataset and the experimental outcome is investigated under different aspects. The results demonstrate the effective diagnostic performance of the presented model with the sensitivity of 97.90%, specificity of 97.91%, and accuracy of 97.56%.


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    Title :

    An Optimal Multilevel Thresholding based Segmentation and Classification Model for Brain Tumor Diagnosis


    Contributors:


    Publication date :

    2020-11-05


    Size :

    544376 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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