Brainai-Net: An Automated Deep Learning Approach For Tumor Detection

This study explores the development of a deep learning-based model for the automated detection of brain tumors, aiming to improve diagnostic accuracy and efficiency compared to existing methods. The research highlights the strengths and limitations of each model, providing a comprehensive analysis to identify the most effective approach for clinical application. The findings suggest that the proposed model offers significant potential for enhancing brain tumor detection, contributing to more accurate and timely diagnoses, and ultimately improving patient outcomes. To address these challenges, this study explores the integration of machine learning techniques, specifically focusing on brain tumor recognition analysis using deep learning. In this research, we leverage deep learning models, including EfficientNetB4, VGG19, VGG16, InceptionV3, Xception, NASNetMobile and Proposed Model, for the automated detection of Brain Tumor. The datasets, based on brain tumor features contribute to the comprehensive analysis. The study aims to streamline the diagnosis process by automating the identification of potential indicators of Brain Tumor, thereby facilitating early intervention. Comparative analysis of the models reveals varying accuracies, with our proposed model demonstrating notable performance in brain tumor recognition, achieving an accuracy of 98.24%. These results underscore the potential of deep learning techniques in enhancing the precision and efficiency of brain tumor diagnosis. The integration of advanced technology to complement existing diagnostic methods, offering a promising avenue for early brain tumor detection. The implications of this research extend beyond conventional clinical approaches, emphasizing the importance of timely interventions and paving the way for improved outcomes in individuals at risk of Brain Tumor.