Brain Tumor Detection Techniques: A Review

The identification, segmentation, and extraction of tumor-affected regions from Magnetic Resonance Imaging (MRI) scans are important tasks in medical diagnosis. These processes are usually performed by radiologists or medical experts and require significant time and experience. Image processing techniques help in visualizing the anatomical structures of human organs more effectively, but detecting abnormal structures in the brain using basic imaging methods is still difficult. In this study, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) method is proposed for brain tumor segmentation using deep learning techniques. The approach introduces a fully automated algorithm that separates the cerebral venous system in MRI images by utilizing structural, morphological, and relaxometry information. The segmentation process ensures a high level of consistency between the brain anatomy and the surrounding tissues. Extreme Learning Machine (ELM), which contains one or more hidden layers, is applied as a learning algorithm and is commonly used for tasks such as regression and classification. In this work, a probabilistic neural network classifier is used to train and evaluate the detection accuracy of tumors in brain MRI images. Experimental results demonstrate that the proposed system can effectively distinguish between normal and abnormal brain tissues with an accuracy of approximately 98.51%, showing the effectiveness of the proposed method.