Advancing Medical Imaging with Capsule Networks for Diagnostic Accuracy
DOI:
https://doi.org/10.22399/ijcesen.1082Keywords:
Medical Imaging, Capsule Networks, Diagnostic Accuracy, Disease Detection, Healthcare InnovationAbstract
The use of capsule networks into medical imaging as a means of advancing medical imaging is a possible path for improving diagnostic accuracy. The objective of this study is to enhance the interpretation and categorization of medical pictures by making use of the hierarchical and pose-sensitive representations that are made available by Capsule Networks. The purpose of this project is to improve the capability of machine learning models to reliably identify and categorize abnormalities, lesions, and other pathological findings in medical imaging data. This will be accomplished by capturing detailed spatial connections and including perspective invariance. The major goal is to improve doctors' early diagnosis abilities to improve patient outcomes and treatment times. When it comes to situations in which typical convolutional neural networks could have difficulty dealing with complicated structures or changes in position and appearance, this method is very helpful. Capsule Networks have the potential to improve medical imaging diagnostics by offering interpretable and contextually rich representations. This would enable physicians to have access to technologies that are more reliable and efficient for illness identification and diagnosis.
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