| Proj No. | A3107-251 |
| Title | BRAIN TUMOR DETECTION USING DEEP LEARNING |
| Summary | Medical experts use the MRI/CT scans for various types of cancer detection; however, a challenging problem in MRI/CT data is the presence of noise, which can lead to misdiagnosis. Thus, the purpose of this project is to develop a deep learning-based model to detect and classify brain tumours from MRI scan images. This project will use Convolutional Neural Networks (CNNs) for image classification, which Can differentiate between benign, malignant, and normal brain tissues, which will help radiologists in early diagnosis of brain tumours. Student will use publicly available Brain MRI datasets from Kaggle, BraTS, or OpenNeuro. Deep Learning Model will use CNN architectures like VGG16, ResNet50, or InceptionNet. Model Evaluation will be done using accuracy, precision, recall, and F1-score for performance assessment and by comparing model predictions with radiologist reports. It is expected that this process will enable the medical practitioners in the diagnosis of various types of diseases such as cancer. |
| Supervisor | A/P Mohammed Yakoob Siyal (Loc:S2 > S2 B2A > S2 B2A 28, Ext: +65 67904464) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering I (Loc: S2-B4c-15) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |