Brain Tumor Detection and Classification Using Artificial Intelligence
The rapid advancement of artificial intelligence (AI) has led to innovative solutions in the medical field, particularly in the domain of medical image analysis. This project introduces a novel approach, “Brain Tumor Detection and Classification Using Artificial Intelligence,” aimed at accurate and efficient identification of brain tumors. The proposed system integrates cutting-edge technologies including the YOLOV2 algorithm for tumor detection and the MobileNetV2 architecture for tumor classification.
The project is implemented in the MATLAB environment, leveraging its powerful image processing capabilities and AI toolboxes. The YOLOV2 algorithm, known for its real-time object detection capabilities, is employed to accurately locate brain tumors in magnetic resonance imaging (MRI) scans. This initial detection phase plays a crucial role in the subsequent classification process. The system focuses on classifying tumors into two categories: Benign and Malignant, which are significant indicators for effective treatment planning.
For tumor classification, the MobileNetV2 architecture is adopted due to its efficiency and effectiveness in handling medical image data. This architecture is fine-tuned using the dataset comprising MRI images of brain tumors. The fine-tuning process involves training the model on the labeled dataset to enable it to distinguish between benign and malignant tumors. The utilization of MobileNetV2 ensures a balance between computational efficiency and classification accuracy.
The performance of the developed system is evaluated using standard metrics, with a particular emphasis on accuracy. The achieved accuracy of 97.14% demonstrates the system’s robustness and high precision in accurately detecting and classifying brain tumors. The integration of YOLOV2 for detection and MobileNetV2 for classification yields a comprehensive solution for addressing brain tumor-related challenges.
In conclusion, the project “Brain Tumor Detection and Classification Using Artificial Intelligence” showcases the potential of AI-driven medical image analysis. The utilization of MATLAB, YOLOV2, and MobileNetV2 contributes to a synergistic framework capable of accurate tumor detection and classification. This project holds great promise in aiding medical professionals by providing timely and precise information about brain tumor status, thereby facilitating more informed decision-making and improved patient care.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
YOLOV2 Algorithm & MobileNetV2 Architecture.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : MATLAB
- Tool : MATLABR2021A
Shubhangi Solanki; Uday Pratap Singh; Siddharth Singh Chouhan; Sanjeev Jain, “Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview”, IEEE Access (Volume: 11), 2023.