Automated Brain Tumor Detection and Classification using Deep learning

Objectives

Develop a Flutter mobile app for brain tumor classification. Integrate a pre-trained deep learning model into the app. Enable users to upload MRI scan images through the app. Use the deep learning model to analyze the uploaded images. Provide a "Yes" or "No" answer indicating the presence of a brain tumor. Enhance diagnostic accuracy and support timely treatment decisions.

Socio-Economic Benefit

Facilitates early detection of brain tumors, leading to prompt medical intervention. Reduces diagnostic delays, potentially improving treatment outcomes. Minimizes healthcare costs associated with delayed or misdiagnosed cases. Enhances access to accurate brain tumor diagnosis, particularly in underserved regions. Improves patient satisfaction by providing timely and reliable diagnostic assistance.

Methodologies

Data Collection: Gather a diverse dataset of MRI scans containing both tumor and non-tumor images. Preprocessing: Prepare the MRI images for model training by standardizing, resizing, and augmenting the data. Model Selection: Choose an appropriate deep learning architecture for brain tumor classification, such as Convolutional Neural Networks (CNNs). Model Training: Train the selected model on the prepared dataset to learn the features indicative of brain tumors. Model Evaluation: Assess the trained model's performance using metrics like accuracy, precision, recall, and F1-score. Integration with Flutter App: Implement the trained model into the Flutter mobile app using suitable frameworks or libraries. App Development: Develop the user interface and functionality of the Flutter app for image uploading and prediction display. Testing and Validation: Validate the integrated system's performance through extensive testing with diverse MRI images. Deployment: Deploy the finalized app on relevant platforms for public access, ensuring scalability and reliability.

Outcome

Development of a Mobile Application: Creation of a user-friendly Flutter mobile app for brain tumor classification. Integration of Deep Learning Model: Successful integration of a pre-trained deep learning model into the app for automated tumor classification. Enhanced Diagnostic Capabilities: Provision of a reliable tool for users to upload MRI images and receive instant "Yes" or "No" predictions regarding the presence of brain tumors. Improved Access to Diagnosis: Increased accessibility to accurate brain tumor diagnosis, potentially benefiting individuals in remote or underserved areas. Facilitation of Timely Treatment: Enablement of early detection through timely identification of brain tumors, potentially leading to improved treatment outcomes and reduced healthcare costs. Contribution to Healthcare Technology: Advancement of healthcare technology by leveraging deep learning and mobile app development to address critical diagnostic challenges.

Project Team Members

PROJECT GALLERY

Automated Brain Tumor Detection and Classification using Deep learning

23 May, 2024 11:50 am