Efficient brain cancer identification using ResNet50 and ResNet50 V2: a comparative study with a primary MRI dataset

Abstract

Primary malignant brain tumors along with central nervous system cause a significant amount of deaths every year, making brain cancer a major worldwide health problem. In South Asian countries, the number of patients suffering from brain cancer is steadily rising. Treatment effectiveness and improved patient outcomes depend on early detection. Using a dataset consisting of 6056 original raw MRI scans, this study evaluates how well convolutional neural networks (CNNs) diagnose brain cancer. We present ResNet50 and ResNet50V2 models assessed for their effectiveness in identifying brain cancers. Transfer learning and fine-tuning were employed to enhance model performance. The models demonstrated strong performance, with 87-99% accuracy rate. ResNet50V2 achieved the highest 99% accuracy. To detect tumor early, this work emphasizes how well the CNN-based machine learning methods help as timely intercession and patient care is necessary. Early prediction with 100% confidence and reliable precision is a critical issue in the modern world. Our goal is to use advanced algorithms to forecast images affected by cancer. Lastly, we will deploy an automated system that will enable us to confidently identify images affected by cancer. Our suggested methodology and its application could significantly impact the field of medical science by combining computer vision and health informatics.