Artificial intelligence and multiple sclerosis: MRIs segmentation and prediction of patients related info
Petroni, Leonardo (A.A. 2022/2023) Artificial intelligence and multiple sclerosis: MRIs segmentation and prediction of patients related info. Tesi di Laurea in Data science in action, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 90. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Multiple sclerosis. Dataset presentation. Machine learning fundamentals. Deep learning. Introduction to image segmentation and the use of autoencoders. ANN. Convolutional neural network architecture. Difference between fully connected networks and fully convolutional networks. Code implementation: architecture choices. Preprocessing and modeling. Training. U-net architecture in detail. Downsampling. Upsampling. Dataset creation. Classifier. Utils module. Training the autoencoder. Training and test the classifier. Power of custom-trained U-Net autoencoder. The significance of accurate MS lesion segmentation. Fight the challenges.
References
Bibliografia: pp. 87-89.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Data science in action |
| Thesis Supervisor: | Italiano, Giuseppe Francesco |
| Thesis Co-Supervisor: | Sinaimeri, Blerina |
| Academic Year: | 2022/2023 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 22 May 2024 12:05 |
| Last Modified: | 22 May 2024 12:05 |
| URI: | https://tesi.luiss.it/id/eprint/38601 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
