A Revolutionizing Procedure to Predict Brain Tumors in Earlier Stages Using MRI Assisted Deep Learning Model

Cancer is regarded as one of the most invasive and harmful diseases, which results in a reduction in the average lifespan of patients. In recent years, there has been a substantial rise in the relevance of studies examining ways to detect brain tumors at an early stage. The patient's survival rate is enhanced by the early detection of a brain tumor for primary treatment. The commencement of treatment plans that enhance the survivability of patients with brain tumors is contingent upon the accurate early medical diagnosis of the tumor. Due to the large volume of images sent into the interpretation system, processing overhead is a significant obstacle when using Magnetic Resonance Imaging (MRI) for early tumor identification. Computer-aided diagnostic systems, such as magnetic resonance imaging (MRI), have consistently demonstrated their effectiveness in assisting medical professionals in the determination of precise diagnoses and have made significant progress in the field of deep learning. In this paper, a novel deep learning approach, Neural Learning with Optimization Strategy (NLOS), is introduced to predict brain tumors in their early stages using MRI images. In order to determine how effective the proposal is, it is cross-validated using the standard deep learning model, the Convolutional Neural Network (CNN). Because of their superior learning capacity and fine processing capabilities, proposed NLOS have proven to be an improvement over existing automation systems in the diagnosis of brain tumors, allowing for faster and more accurate processing. The present developments in the digitization of brain tumor recognition, as well as the advantages, constraints, and possible futures of existing methods such as CNN for computer-aided evaluation in brain tumor detection, are delineated. The proposed model is more advantageous than other conventional models in that it can predict brain tumors at earlier stages with an accuracy ratio of 98.63%.