The human brain is the most interesting and intricate mechanism in the human body which is comprised of hundreds of billions of neurons and that has prompted a considerable lot of research of the organ. Some of the primary activities of the human brain are to govern muscles, and coordinate bodily movement, sensory perceptions, memory, learning, speech, emotions, intelligences and consciousness. The abnormal proliferation of cells in brain leads to the establishment of the tumor in brain. In this study effort, an automated brain tumor detection and segmentation technology is suggested. The suggested technique comprises of feature extraction, classification and segmentation. In this study, Gray Level Co-occurrence Matrix (GLCM) based features, Discrete Wavelet Transform (DWT) co-efficient and Laws texture features are employed. These characteristics are learned and categorised into either normal or pathological using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. Morphological procedures are conducted on the categorized abnormal brain imaging in order to separate the tumor areas.

Segmenting brain tumours is a critical part of medical image processing. Early detection of brain tumours is critical for enhancing treatment options and boosting the overall survival percentage of patients. When it comes to cancer diagnosis, manual segmentation of the brain tumours from huge amounts of MRI data obtained in clinical practise may be complex and time intensive. Automatic tumour picture segmentation is required. This article provides a basic introduction to the brain and the many disorders that might arise in it. This paper clearly outlines the differences between benign and malignant brain abnormalities. Conventional methods for detecting and sizing brain tumours are also discussed in this study Techniques for diagnosing and treating a brain stroke are also discussed in detail. For the identification of brain tumours and strokes, present algorithms have certain limitations.