Early and reliable detection of Brain lumps including mening and gliome, remains a significant concern in clinical practice. Conventional Magnetic Resonance Imaging (MRI) analysis relies heavily on expert interpretation, which may introduce variability and delay diagnosis. This study describes an integrated deep learning-based system for automated brain tumor detection. The developed system, termed the Automated Neuro-Diagnostic Assistant (ANDA), is designed using a customized Convolutional Neural Network (CNN) trained on preprocessed MRI datasets. \ key feature of the system is its deployment as an interactive real-time web application using Flask, incorporating modules such as a confidence score visualizer and an automated report generator. Experimental results indicate a classification accuracy of 96% along with an F1-score of 94%, demonstrating reliable prediction capability. The proposed evaluation metric (Fscore) provides a unified assessment of system performance by combining accuracy, interpretability, and usability factors. Fscore = ⌊0. 35A + 0. 25C + 0. 15V + 0. 10 (HI + SI + UI) ⌉ Where A represents classification accuracy, C denotes confidence reliability, V indicates the effectiveness of visual interpretation, HI refers to healthcare insight generation, SI represents stroke identification capability, and UI denotes overall system usability. Further evaluation on diverse MRI samples shows strong agreement (r = 0. 89) with expected diagnostic patterns and consistent performance across tumor categories. The system improves interpretability and user interaction efficiency, enabling faster and more structured medical image analysis. Overall, the system provides stable processing, real-time prediction capability, and efficient handling of medical data. By integrating automated detection with supportive visual outputs, it enhances accessibility to AI-assisted diagnostic tools and supports preliminary medical assessment. \
Yadav et al. (Thu,) studied this question.