Brain Tumour Region Extraction Using Novel Self-Organising Map-Based KFCM Algorithm

Medical professionals need help finding tumours in the ground truth image of brain because tumours’ location, contrast, intensity, size, and shape vary between images different acquisition methods, modalities, patient’s age. The medical examiner has difficulty manually separating a tumour from other parts Magnetic Resonance Imaging (MRI) image. Many semi- fully automated detection systems have been written about literature, they keep improving. segmentation literature seen several transformations throughout years. An in-depth examination these methods will be focus this investigation. We look at most recent soft computing technologies used MRI analysis through review papers. This study looks Self-Organising maps (SOM) with K-means kernel Fuzzy c-means (KFCM) method for segmenting them. suggested SOM networks were first compared to an experiment based on datasets well-known cluster solutions. Later, is combined KFCM, reducing time complexity producing more accurate results than methods. Experiments show that skewed data improves networks’ performance SOMs. Finally, measures real-time are analysed using machine learning approaches. proposed algorithm good sensitivity better accuracy k-means state-of-art