Autoencoder-Based Brain Tumor Detection Using Deep Learning Methods

Brain tumors serve as a significant healthcare challenge on a global scale. Effective treatment planning depends on accurate and prompt detection. The most common method for finding brain tumors uses magnetic resonance imaging (MRI), which produces complex image data that needs to be analyzed by a skilled radiologist. However, because of the complexity and variety of brain tumors, manual evaluation is prone to errors. In our research, we make use of an extensive dataset made up of several forms of brain tumors, such as glioma tumors, pituitary tumors, meningioma tumors, and non-tumor images. We intend to construct and assess deep learning model for detecting and classifying brain tumor precisely using this dataset. The model will be made to take use of the distinctive traits and spatial information included in MRI images, allowing for the accurate detection and differentiation of diverse tumor types. The study aims to indicate the superiority of deep learning algorithms over manual categorization techniques and conventional methodologies for improved brain tumor diagnosis. The findings demonstrate better brain tumor detection and classification capabilities which are helpful especially in areas with limited access to specialized radiologists. This study demonstrates how deep learning algorithms have the potential to revolutionize the identification and categorization of brain tumors. Our proposed model with an impressive accuracy of 99.97% aims to increase treatment planning, improve patient care, and contribute to better outcomes for patients with brain tumors.