Segmentation and classification of brain tumour using LRIFCM and LSTM

Brain tumour is an abnormal growth of cells in the brain, and is a harmful and life-threatening disease worldwide. The rapid development of tumour cells increases the illness and death rates. Hence, the timely detection and classification of brain tumours is crucial for saving thousands of lives. However, the diagnosis of brain tumour is a challenging task because of different brain shapes, sizes and synaptic structures. In this research, the Linear Intensity Distribution Information (LIDI) and regularization parameter based Intuitionistic Fuzzy C-Means Algorithm (IFCM), namely LRIFCM is proposed for an effective segmentation of the brain portion that is affected by tumours. The different feature extraction approaches namely, LeNET, Gray-Level Co-occurrence matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract appropriate features from the brain images. Further, the Long Short-Term Memory (LSTM) classifier is used to classify the types of tumour based on the extracted features. Three different datasets namely, BRATS 2017, BRATS 2018 and Figshare brain datasets are used to analyse the proposed LRIFCM-LSTM method. The LRIFCM-LSTM is evaluated using both the segmentation and classification results. The segmentation using LRIFCM is assessed based on the parameters of SSIM, Jaccard, dice, accuracy and sensitivity, whereas the classification using LSTM is assessed based on accuracy, specificity, sensitivity, precision and F1-score. The existing researches: Hybrid-DANet, TECNN and VAE-GAN are used for comparison with the LRIFCM-LSTM method. The classification accuracy of LRIFCM-LSTM for BRATS 2018 dataset is 98.73 which is higher when compared to the TECNN.