As a result, to develop a radiological decision support system, it would need to be equipped with potentially hundreds of deep learning models with each model trained for a specific task or organ.
A current clinical challenge is identifying limb girdle muscular dystrophy 2I(LGMD2I)tissue changes in the thighs, in particular, separating fat, fat-infiltrated muscle, and muscle tissue.
Therefore, we applied our multiparametric radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II and 16 grade IV).
Radiomics is a rapidly growing field that deals with modeling the textural information present in the different tissues of interest for clinical decision support.
For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images.
On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map.