Brain Tumor Classification and Severity Analysis Using Deep Learning and Machine Learning

Project Code :TCMAPY2245

Objective

The primary objective of this project is to develop a comprehensive system that effectively classifies brain tumors and predicts their severity based on both medical imaging and clinical data. This includes the implementation of deep learning models, such as Convolutional Neural Networks (CNN), ResNet, MobileNet, and Inception, to classify brain tumor images into categories like glioma, meningioma, pituitary, and no tumor. Additionally, the project focuses on utilizing machine learning models like XGBoost, LightGBM, and RandomForestClassifier to predict tumor severity stages (I, II, III, IV) using clinical features such as age, tumor size, survival rate, and treatment history. Data preprocessing and feature extraction will play a critical role in preparing both image and clinical data for model training. A user-friendly web application will be developed with a frontend using HTML, CSS, and JavaScript and a backend built with Python and Flask, enabling users to upload medical images and input clinical data for tumor classification and severity analysis. Performance evaluation of the models will be conducted using accuracy, precision, recall, and F1-score to ensure reliable predictions. Ultimately, the project aims to provide a scalable, interpretable tool to aid healthcare professionals in making informed decisions regarding brain tumor diagnosis and treatment planning.

Abstract

Brain tumor classification and severity analysis play a critical role in the early detection and treatment planning of brain-related health conditions. This project presents a comprehensive approach to classifying brain tumors from medical imaging and assessing tumor severity using machine learning techniques. The system utilizes a combination of deep learning algorithms such as Convolutional Neural Networks (CNN), ResNet, MobileNet, and Inception for the classification of brain tumor images into categories like glioma, meningioma, pituitary, and no tumor. Additionally, the project aims to predict tumor severity stages (I, II, III, IV) based on clinical attributes such as age, tumor size, survival rate, tumor growth rate, gender, and treatment history using machine learning models like XGBoost, LightGBM, and RandomForestClassifier. The system has been developed with an easy-to-use web interface, built using HTML, CSS, and JavaScript for the frontend, and Python with the Flask framework for the backend. By combining these state-of-the-art algorithms and technologies, this project provides an effective tool for both classifying brain tumor images and analyzing the severity of the disease. The results demonstrate the efficacy of machine learning models in providing valuable insights for medical professionals to aid in decision-making.

Keywords: Brain tumor classification, severity analysis, CNN, ResNet, MobileNet, Inception, machine learning, XGBoost, LightGBM, RandomForestClassifier.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.