Style transfer and Image manipulation
Given any photo this project should be able to take any art movement such as Picasso’s Cubism and apply the style of the art movement to the photo. The neural network first needs to detect all the subjects and separate them from the background and also learn about the color schemes of the photo. Then the art movement datasets needs to be analyzed to find patterns in the style. The style will then need to be applied to the initial photo. The final rendering of the photo could get a little computationally expensive, if that is the case there will be need for GPU hardware. Imaging libraries such as pillow and scikit would be needed. It might be a little hard to find proper datasets since there are limited datasets available for each art movement. Contrarily I could rid myself of the need for readily-made datasets by training the network to detect style patterns by feeding it unlabeled paintings.
Image manipulation detection
Neural network would be trained to detect image manipulation in a given photo. There are many ways to achieve this including but not limited to image noise analysis. Different algorithms can be compared to see which can do the best detection manipulation or which one was better automated with the training process.
Python libraries such as Keras and sklearn will be used for the Neural Network and the deep learning. Many previous research papers and datasets are available for this project or similar ones.
Radiology disease detection
Trained neural networks for detecting radiology abnormalities and diseases have reached a level that can easily compete with a human radiologists. For this project I will be using neural network libraries to detect different abnormalities. There are very different field that this can apply to such as: Brain tumor detection, breast cancer detection, colonoscopy, CT/MRI, oncology, etc. I have already found many datasets for some of these applications. Again this is a very rich field with a lot of previous work and published papers to explore.