Brain tumour detection and classification using hybrid neural network classifier Online publication date: Mon, 01-Mar-2021
by Krishnamurthy Nayak; B.S. Supreetha; Phillip Benachour; Vijayashree Nayak
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 35, No. 2, 2021
Abstract: Brain tumour is one of the most harmful diseases, and has affected majority of people in the world including children. The probability of survival can be enhanced if the tumour is detected at its premature stage. Moreover, the process of manually generating precise segmentations of brain tumours from magnetic resonance images (MRI) is time-consuming and error-prone. Hence, in this paper, an effective technique is employed to segment and classify the tumour affected MRI images. Here, the segmentation is made with adaptive watershed segmentation algorithm. After segmentation, the tumour images were classified by means of hybrid ANN classifier. The hybrid ANN classifier employs cuckoo search optimisation technique to update the interconnection weights. The proposed methodology will be implemented in the working platform of MATLAB and the results were analysed with the existing techniques.
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