Digital Image for Brain Tumor Detection Using MRI Aiming to Improve Accuracy and Early Diagnosis Through a Neural Diagnostics System

This research project focuses on developing an efficient method for identifying brain tumors in MRI images through the utilization of transfer learning and convolutional neural networks (CNNs). The study involves analyzing 2800 brain MRI images obtained from a Korean hospital in Ethiopia, with the goal of distinguishing between healthy brain scans and those indicating the presence of tumors based on characteristics such as size, shape, and patterns. The proposed detection system leverages pre-trained models like VGG16 or Inception V3 and integrates deep learning techniques with transfer learning. Performance evaluation of the model is conducted using metrics such as precision, recall, and F-1 score, providing insights into its accuracy. Various optimization strategies are employed to enhance the accuracy of the model, resulting in a classification accuracy of 93.10%. To prevent overfitting, data augmentation techniques are applied, and the study recommends the use of standard datasets for future experimentation. Additionally, the importance of large-scale datasets for improving the performance and generalization capabilities of machine learning and deep learning algorithms is highlighted.