Enhancing Brain Tumor Detection Using a CNN-Powered Android Application

Tumors of the brain are a significant clinical prob-lem, which highlights the need for efficient diagnosis at the earliest possible stage to enable effective treatment. Our study explains the development of a brain tumor detection system in real time that is incorporated in an Android application, and it's based on a convolutional neural network (CNN). The suggested system provides a high level of accuracy equal to 99.61%, and can detect four types of brain tumors: gliomas, meningiomas, pituitary tumors, and non-tumorous conditions. Through the use of TensorFlow Lite for mobile deployment, the application guarantees the efficiency of the application as well as working offline, thus making it relevant for real-life use. This study demonstrates that integrating efficient mobile applications with effective deep learning models can help implement real- time brain tumor diagnosis. The results also show how AI-based applications have the possibility of improving brain tumor detection and contributing to early detection.