Brain Tumor Detection Using Convolutional Neural Networks (CNNs)

Abstract

Brain tumors are a serious medical condition that requires early detection for successful treatment. However, accurate diagnosis can be difficult and time-consuming, and current methods such as MRI scans can be expensive and may require highly trained specialists to interpret the results. A model of a brain tumor detection system using Convolutional Neural Networks (CNNs) has been proposed to address these challenges. To use this model, a dataset of medical images of the brain is collected, the dataset is then preprocessed, and the relevant feature is extracted from the images using CNNs. The developed CNN model is designed and trained to accurately detect the presence and location of brain tumors in the images. Optimization of the CNN model's performance is done by experimenting with different architectures, hyperparameters, and optimization techniques, and its performance is evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score. The model training was carried out on MRI images containing tumors and without tumors. The developed CNN-based model achieved impressive accuracy in detecting brain tumors, demonstrating high precision and recall rates. This brain tumor detection system has the potential to significantly improve the accuracy and efficiency of brain tumor diagnosis, leading to better treatment outcomes and reducing the burden on healthcare systems.