Accurate Brain Tumor Diagnosis with LeNet5: A New Frontier in Medical Imaging

The system analysis in this research work involved using the LeNet-5 architecture for the identification of brain tumors, which is one of the major tasks in medical image analysis. LeNet-5 is a CNN, which is popular among machine learning engineers for image classification, due to its simplicity in handling image data. The system architecture is organized as a series of convolutional and then fully connected layers meant for extracting spatial hierarchies from images. In this research, we focused on classifying brain tumor images into two categories: A positive finding is indicated by "Yes" for the presence of a tumor while a negative by "No" for the absence of a tumor. The proposed network was trained, and its effectiveness was analyzed, using a prepared set of brain tumor images and evaluating metrics including accuracy, precision, recall, and F1-score. Basically, for better understanding, let us evaluate the model’s accuracy for 50 epochs, in which the model achieved an accuracy of approximately 99.73 by the last epoch. The validation accuracy also increased over time and landed in a later epoch at 92.68 percent and therefore affirmed the stability ranging well of the LeNet-5 model for this classification. The reported precision and recall metrics for both classes (Yes and No) were considerably high, as it was possible to accurately classify images as belonging to the group of images with brain tumor or non-tumor. In general, the LeNet-5 model demonstrated high accuracy and can be regarded as suitable for solving the tasks of detecting brain tumors in medical imaging.