The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.

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In computer vision, we must handle with the various structural aspects of image or video data. The texture is one of the most important aspects of this type of data, which is utilised to identify objects or regions of interest in an image. As imaging conditions change, textures inside actual images significantly change in brightness, contrast, size, and skew. To recognise textures in real-world images, a similarity measure that is invariant to these features must be used. Existing recognition techniques did not perform well due to issues such as illumination, scale, and subject rotation. To address this issue, invariant feature representation methods are being developed to generate features that are insensitive to such variations. In this paper, we proposed a robust hybrid feature descriptor and predicted the faces under illumination, scale, and pose variations using an optimum multi-kernel support vector machine. Additionally, the suggested robust hybrid feature descriptor is enhanced by combining a hybrid transform composed of discrete wavelet and discrete shearlet transforms with some image statistical textural data. The proposed face recognition system is implemented in MATLAB, and analysed using various parameters to show proposed methods improved performance as compared to the state of the art methods.