Research centered on leveraging advanced technology identifying abnormalities. Specifically, the study implements UNet and ResUNet algorithms for segmentation, as well as ResNet and Transformers for classification, aiming to expedition. The methodology entails preprocessing the input data, training the deep comprehensive and fine-tuning networks optimize performance in discerning various tumor types. Through extensive testing and validation on diverse datasets, the findings showcase improved accuracy and novelty in diagnostic capabilities. This innovative approach not only streamlines the diagnostic process, reducing the time required for precise diagnosis, but also diminishes the potential for human error, thereby elevating the overall standard of neurology healthcare delivery.
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Harish et al. (2024) studied this question.
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