Main Article Content
Bio-medical technology now plays a critical role in the detection and treatment of a wide range of diseases, from minor to life-threatening. One of the most life-threatening disorders is brain tumor, which is defined as a mass development of abnormal cells in the brain. By avoiding the spread of aberrant cells, early discovery and treatment can save a person's life. In the medical field, it is vital to find a certain image categorization strategy based on tumor cell regions. The tumor region is then selected to perform the segmentation process and then classification is performed. The identification-based method helps to limit the image area and to identify the border area in a reduced time period. Automatic brain tumor classification is a difficult undertaking due to the enormous geographical and structural heterogeneity of the brain tumor's surrounding environment. The use of Deep Neural Networks classification for automatic brain tumor detection is proposed. The proposed a Relatable Pixel Extraction with Magnetic Resonance Imaging (MRI) Image Segmentation for Brain Tumor Cell Detection (RPEIS-BTCD) using Deep Learning Model. The proposed model is compared with the existing models and the results indicate that the proposed model performance the accuracy is 97%.