Hey there! As someone interested in the intersection of healthcare and technology, I came across a fascinating Reddit post about a brain tumor detection project using deep learning. The project aims to classify tumors into four categories: glioma, meningioma, pituitary, and no tumor. What caught my attention was the student’s enthusiasm to go beyond just accuracy and create something that can make a real impact.
The project has already achieved an impressive 83-85% test accuracy using ResNet50 with transfer learning. To take it to the next level, the student is seeking guidance on feature suggestions, model recommendations, and ideas for evaluation metrics. It’s inspiring to see the willingness to explore different approaches, including the use of EfficientNetV2, Vision Transformers, InceptionV3, DenseNet121, and MobileNet for edge deployment.
One aspect that resonated with me was the importance of exploring beyond just accuracy. The student is looking to estimate tumor size roughly from heatmaps, which could lead to more insightful and practical applications. I think this project has the potential to make a significant difference in the medical field, and I’m excited to see how it unfolds.
What do you think? Have you worked on similar projects or have any suggestions for the student? Share your thoughts in the comments below!