Which type of machine learning is most commonly used in medical image applications?
Mostly, the images are of similar kind I.e. x-rays/ MRI or CTScans if we consider analysis of monochromatic films. In other cases, we can have microscopic images for anayslsis. In all such cases, I found that transfer learning performs much evidently. I have experienced progressive resizing generate excellent results if used with transfer learning. I recommend read of these works https://peerj.com/articles/cs-348/
And
https://pubmed.ncbi.nlm.nih.gov/34109279/
Computer vision for image analysis it's a bautiful field! Depending on your need the algorithm may vary:
Simple classification for patterns and known features: SVM, K-means clustering, random forest among other ML techniques.
Complex recognition of patterns: DL is your best bet.
If you are expirienced with tech, I would recomend blindly the use of visual transformers; but by far the most documented technique are convolutional neural networks.
Depending on your goal you may try some flavors of CNN. If you wish to work with X-Rays I would recomend pretrained models from U-net or VGG16.
At the end of the day, a good dataset of dicom, fmri, jpg (or even MEG) can be put into a neural netowrk and recieve interesting results.
Convolutional Neural Networks (CNNs) are the most commonly used type of machine learning in medical image applications. CNNs are a deep learning method that is particularly well-suited to image analysis tasks due to their ability to automatically learn hierarchical feature representations from images. They have been successfully applied to a wide range of medical image analysis tasks, including segmentation, classification, registration, and reconstruction. For example, they have been used for segmentation of organs and tumors in MRI and CT scans, classification of different types of breast cancer in mammograms, and reconstruction of 3D images from 2D projections in CT and PET scans. CNNs have been shown to outperform traditional machine learning methods and are often used as a key component in state-of-the-art medical image analysis systems. However, the use of CNNs in medical image applications requires large datasets, specialized hardware, and careful tuning of hyper-parameters to achieve optimal performance.
Some of the most common types of machine learning algorithms in medical image applications are CNNs, RNNs, SVMs, Random Forests, and Transfer Learning.
Applications of Artificial Intelligence and Machine Learning in 2023 (emeritus.org)
It's important to note that while CNNs are widely used, other machine learning techniques, such as random forests, support vector machines (SVMs), and deep learning architectures like recurrent neural networks (RNNs), may also find applications in medical image analysis depending on the specific task and dataset.
Check this paper: Article Deep learning modelling techniques: current progress, applic...
hi. can i know if can we used Knn for machine learning radiomic of cervical cancer to evaluate DWI-MRI Bvalue parameter. pls advice. tqvm in advance..
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