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BMO based improved Lite Swin transformer for brain tumor detection using MRI images by Venkata Subba Reddy Gade; Reni K Cherian; Bhupathi Rajarao; Madam Aravind Kumar is a Medicine article available to read on EtoBox.

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Magnetic resonance imaging (MRI) of the brain is the gold standard for diagnosing degenerative neurological conditions such as brain tumours, strokes, dementia, and Multiple sclerosis (MS). For the purpose of health monitoring, MRI scans, a plethora of deep learning-based medical image processing approaches have been presented. After a fully linked network, a convolution neural network (CNN) is the next best structure. While it excels at improving local features and is translation invariant, it has trouble getting global features. In order to make up for CNNs' flaws and efficiently get global characteristics, transformers can be used. Unfortunately, the calculated number of transformers is too high. An improved Lite Swin transformer (OLiST) is offered as a viable solution to this issue. msOur proposed OLiST model takes advantage of both the global feature acquisition capability of the Lite Swin transformer and the capability of the CNN by combining the features extracted by the two and then sampling them up gradually. The barnacles mating optimizer (BMO) for hyper-parameter optimization in the LiST model enhances its performance in detecting brain tumours. Kaggle database photos of

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Author

Venkata Subba Reddy Gade; Reni K Cherian; Bhupathi Rajarao; Madam Aravind Kumar

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Medicine (Physical Sciences)