Design of an Iterative Cluster-Based Model for Detection of Brain Tumors Using Deep Transfer Learning Models

A tumor develops when brain cells exhibit abnormal growth patterns within various body locations, characterized by irregular boundaries and shapes. Typically, these tumors exhibit rapid proliferation, increasing at a rate of approximately 1.6% per day. AThis abnormal cell growth can lead to invisible illnesses and alterations in psychological and behavioral functions, contributing to aArising trend in adult mortality rates worldwide. Therefore, Brain tumors must be detected early. Failure to do so may cause a deadly, incurable condition. Effective brain tumor therapy improves survival if detected early. Magnetic Resonance Imaging (MRI) is essential for finding and classifying brain tumors. The manual nature of brain tumor diagnosis and classification makes it prone to errors, necessitating the development of automated processes for improved accuracy. In light of these considerations, we have devised with a fully automated way to use MR images to find and classify brain tumors. Our approach encompasses three key phases: pre-processing, segmentation, and classification. To detect tumors in the brain, we utilized MRI, employing the deep transfer with the transformed VGG19 model. Notably, our research demonstrates superior growth rates when using other pre-trained Convolutional Neural Network (CNN) models such as AlexNet and VGG-16. AThe deep transfer learning with the transformed VGG19 model yielded accuracy achieving 98.65% (dataset 1) and 99.18% (dataset 2) for different datasets.