Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis.

ABSTRACT:

Background

The best possible treatment planning and patient care depend on the precise diagnosis of brain diseases made with medical imaging information. Magnetic resonance imaging (MRI) is increasingly used in clinical score prediction and computer-aided brain disease (BD) diagnosis due to its outstanding correlation. Most modern collaborative learning methods require manually created feature representations for MR images. We present an effective iterative method and rigorously show its convergence, as the suggested goal is a non-smooth optimization problem that is challenging to tackle in general. In particular, we extract many image patches surrounding these landmarks by using data to recognize discriminative anatomical characteristics in MR images. Our experimental results, whi

SUBMITTER: K P 

PROVIDER: S-EPMC11784773 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature