Enhancing Brain Tumor Detection Through Deep Learning: A Comparative Study of CNN and Pre-trained VGG-16 Models

In this investigation, we present a comprehensive analysis of deep learning approaches employed in the detection of brain tumors. Our focus centers on evaluating the effectiveness of two distinct classification models: a CNN sequential architecture and a pre-trained VGG-16 model. Through a comparative review, we shed light on the unique advantages and disadvantages of these methodologies. Our study offers valuable insights into optimal deep-learning algorithms for diagnosing brain tumors, derived from thorough experimentation and analysis. After assessing multiple models, we observed that our proposed model exhibited remarkable accuracy compared to others. Analyzing a dataset comprising 7023 MRI images, our CNN model achieved an outstanding accuracy rate of 96%. These findings contribute significantly to ongoing endeavors aimed at improving the identification and treatment of brain tumors, serving as a valuable resource for medical image analysis and deep-learning researchers and practitioners.