Fractional Sobel Filter Based Brain Tumor Detection and Segmentation Using Statistical Features and SVM

CapsNet topology to classify tumours from brain images and comparative evaluation

“…This is a challenging task due to (i) in‐homogeneous intensity, (ii) atypical shape of tumours and brain tissue structures, (iii) different types of noise in images. Classification of abnormal brain images, which means the categorisation of brain tumour types. This is a much more challenging task due to similar patterns of tumours. The automated techniques in the literature have usually been applied for detection and segmentation of tumours [12–17] or categorisation of abnormal and normal cases from brain MR images [18–22] instead of classification of tumour types. In these works, abnormality detection was provided by shape, intensity and textural features (e.g.…”

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

“…The presented method is able to merge these modalities containing different images and converted them to make a single image. Padlia and Sharma () presented a brain tumor segmentation approach for T 1 and FLAIR images. A fractional Sobel filter is utilized in this work for tumor enhancement and statistical features are extracted for tumor segmentation.…”

Multi‐objective differential evolution‐based ensemble method for brain tumour diagnosis

“…As a discriminative supervised classifier, SVM defines some separating hyperplanes which combine linear algorithms with linear or non-linear kernel functions. SVM is a powerful tool for analysis of medical data with several applications in classification and regression tasks, compare [6,[30][31][32].…”