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Is pls regression suitable for image classification?

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Hi,
I would like to ask your intuition about the matter. I have multiple samples of very large 3D volumes (MRI-scans) which I would like to classify. Before I move to trying out CNNs I would like to try out the more traditional machine learning methods like PLS + SVM. But do you think that this approach makes any sense in the case of images which have so much intra-class variation (i.e. a single pixel intensity does not correlate with the class)?
Also is the use of plsregress correct with categorical output? Or does matlab have separate partial least square discriminant analysis method?
Best wishes,
Juuso

Risposte (1)

Sai Pavan
Sai Pavan il 16 Feb 2024
Hello Juuso,
I understand that you want to know whether it would be beneficial to try traditional machine learning methods like Partial Least Squares Regression (PLS) + Support Vector Machines (SVM) before trying CNNs for the classification of 3D MRI data.
In the context of MRI scans, the suitability of PLS for classification can be limited as PLS might struggle to capture the informative textures and complex patterns which are crucial in medical imaging. Although PLS reduces the dimensionality of the data, it may not capture the spatial and volumetric information effectively unlike CNNs that are specifically designed to capture spatial hierarchies and patterns in image data.
However, Partial Least Squares Regression can be adapted for classification tasks, including image classification, by using it as a feature extraction or dimensionality reduction method before applying a classifier like SVM on the extracted features. The function plsregress in MATLAB is designed for regression problems with continuous outcomes. For categorical outcomes, PLS can be adapted for classification by using the scores from PLS as input features for a classifier such as SVM with the 'fitcecoc' function.
Please refer to the below documentation to learn more about:
Hope it helps!

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