Brain Tumor Identification and Classification Using Enhanced Convolution Neural Network

Brain tumor identification and classification play a crucial role in medical imaging. Accurately identifying tumors is a challenging task that often requires highly skilled and experienced doctors. Manual identification is prone to errors, leading to the need for automated solutions. This study aims to utilize advancements in Machine Learning (ML) and Artificial Intelligence (AI) to develop an automated brain tumor identification and classification system. The primary objective is to predict the type of tumor accurately. This study employs a deep neural network architectures specifically Convolutional Neural Network (CNN) and other pre trained deep learning models to find the optimal solution that provides a high degree of accuracy in detecting cancerous cells in the brain. In this study, advanced ML and AI techniques are used to perform brain tumor classification. The study utilizes various deep CNN architectures and methods like random cropping to improve training accuracies. Proposed CNN 7x7 model demonstrates remarkable performance in accurately classifying brain tumors. It achieves a high degree of accuracy in detecting the presence of cancerous cells, surpassing manual identification methods and the accuracies of other pre trained models. The automated brain tumor classification system presented in this study has the potential to streamline the diagnosis and treatment process, significantly reducing the reliance on healthcare systems. By reducing the cost and time required for brain tumor diagnosis, it improves patient access to care. Future research can further enhance the proposed methodology and its applications in medical diagnosis.