Texture analysis applied to brain MRI to classify low and high grade gliomas

Suárez-García, José Gerardo; López, Javier M. Hernández; Barbosa, Eduardo Moreno; Hernández, Mario Iván Martínez; Muñoz, G. Tejeda; Alonso, Benito de Celis

doi:10.1063/1.5095912

Search citation statements

Order By: Relevance

Paper Sections

Select...

Literature Review1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Classical machine-learning algorithms have been used for brain tumor grading and other automated medical image analysis tasks. [25][26][27] The limitation of these techniques is the need to handcraft suitable features from the images/volumes, which are then fed to the classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain tumor grade classification using multi‐step pre‐training

Mehmood,

Bajwa,

Anwar

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Medical images offer a non‐invasive method to diagnose different diseases, but using them manually produces unreliable results. Modern deep learning architectures and techniques are computed and data‐intensive, making them difficult to use for relatively smaller datasets of medical images. Transfer learning has been used as a remedy for the problem mentioned above. However, the domain difference between the datasets used for pre‐training (e.g., ImageNet) and the target datasets, like medical images, negatively impacts the transfer learning results. Recently, many researchers have used additional pre‐training called domain‐adaptive pre‐training (DAPT) using the data from the target domain (e.g., medical images) before using the model on the target tasks to achieve superior performance. This study proposes a variant of DAPT by performing it on a subset of the architecture. It has achieved state‐of‐the‐art performance for brain tumor grading on the BraTS 2019 while being computationally efficient.

show abstract

Section: Literature Reviewmentioning

confidence: 99%