Detecting and Diagnosing Brain Tumors Using ML

Abstract

Brain tumors are among the most severe and life-threatening medical conditions, often leading to significant morbidity and mortality if not detected and treated promptly. Early and accurate diagnosis plays a crucial role in improving patient outcomes and survival rates. Traditionally, the detection of brain tumors relies on the manual analysis of Magnetic Resonance Imaging (MRI) scans by radiologists. While effective, this process is inherently time-consuming and subject to variability in interpretation due to human fatigue or inexperience, potentially leading to diagnostic delays or errors. To address these challenges, this project introduces an Automated Brain Tumor Detection and Medicine Suggestion System powered by Convolutional Neural Networks (CNNs) — a class of deep learning models particularly effective in image classification tasks. The proposed system is capable of automatically analyzing MRI scans and accurately classifying them into two categories: “Tumor Detected” and “No Tumor Detected.” By automating the detection process, the system significantly reduces the workload of medical professionals and minimizes the chances of oversight, thereby enhancing diagnostic reliability and efficiency. Beyond tumor detection, the system is designed to provide preliminary medicine suggestions based on the type and severity of the tumor identified, referencing medical databases and treatment protocols. This feature aims to support healthcare professionals by offering instant insights into potential treatment pathways, helping expedite the decision-making process in clinical settings. The integration of AI in medical imaging through CNN-based models not only speeds up the diagnostic process but also democratizes access to expert-level analysis, especially in remote or under-resourced regions. With continuous training on diverse and large datasets, the system can improve its accuracy and robustness, making it a valuable tool in modern healthcare. In conclusion, the proposed system demonstrates the potential of artificial intelligence in revolutionizing medical diagnostics. By merging advanced image processing with intelligent recommendation systems, it offers a promising solution for early brain tumor detection and effective treatment planning, ultimately contributing to better patient care and outcomes.

Introduction

This project focuses on early detection of brain tumors using deep learning, computer vision, and web technologies to improve diagnosis and patient outcomes. It employs a Convolutional Neural Network (CNN) built with TensorFlow and Keras to analyze MRI brain scans and classify images as tumor or non-tumor with high accuracy.

The system preprocesses MRI images using OpenCV for tasks like resizing and noise reduction to enhance model performance. A Flask-based web interface enables users to upload scans and receive instant predictions. It also includes secure user authentication via SQLite.

Key features include automated email notifications with diagnostic reports, medication recommendations based on tumor characteristics, and a hospital locator that uses the user’s location to suggest nearby medical facilities.

The methodology involves data acquisition, preprocessing, CNN-based feature extraction and classification, diagnosis with heatmaps, and performance evaluation. The system workflow covers user authentication, image upload, image segmentation (Otsu thresholding), model training/testing, prediction, and result display.

Implementation modules handle data loading, image processing, model training, user management, and prediction output. The project extends previous work by applying CNNs with preprocessing techniques like data augmentation and normalization to improve accuracy. Transfer learning may be used to leverage pre-trained CNN models for better results on limited medical image datasets.

Overall, the project demonstrates an AI-powered, user-friendly web tool for accurate brain tumor detection and early intervention support.

Conclusion

This project successfully demonstrates the application of deep learning in medical imaging by developing an efficient Brain Tumor Detection system. Utilizing TensorFlow / Keras for model training, OpenCV for image preprocessing, and Flask for deploying a user-friendly web interface, our system provides an accessible and effective tool for early tumor detection. SQLite was integrated for secure and efficient data management, ensuring a streamlined workflow for users. The results indicate that deep learning models can significantly aid in the early diagnosis of brain tumors, potentially improving patient outcomes by facilitating timely medical intervention. While the current implementation offers promising accuracy and performance, future improvements could include expanding the dataset, incorporating more advanced neural network architectures, and integrating explainable AI techniques for better model interpretability. This work underscores the transformative role of AI in healthcare, demonstrating how technology can complement medical expertise to enhance diagnostic capabilities.

References

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Copyright

Copyright © 2025 Lakkakula Jaya Sankar, Gadamsetty Harichandan, Shaik Ajith, Mavilla Bhargav, Ms. Guna Gayathri Praseetha K M.E. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.