Document Type
Event
Start Date
23-4-2023 5:00 PM
Description
Convolutional neural networks are a powerful tool in machine vision when it comes to identifying patterns. Considering the importance of identifying patterns in medical imaging, there is a great opportunity to develop effective CNN models to analyze medical imagery. Furthermore, the introduction of transfer learning has introduced the opportunity to develop models which are more accurate and better generalize to other datasets. Therefore, we explore the efficacy of different CNN models with respect to a dataset regarding X-ray imaging for pneumonia. We do this through using transfer learning models and demonstrate that pretrained models outperform models which are trained only on the dataset available. We also highlight additional methods which could be expanded upon in future research and hope to develop a state-of-the-art model for this particular dataset in the final version of this paper.
UR-369 Classifying Chest X-Rays of Pneumonia Using Transfer Learning
Convolutional neural networks are a powerful tool in machine vision when it comes to identifying patterns. Considering the importance of identifying patterns in medical imaging, there is a great opportunity to develop effective CNN models to analyze medical imagery. Furthermore, the introduction of transfer learning has introduced the opportunity to develop models which are more accurate and better generalize to other datasets. Therefore, we explore the efficacy of different CNN models with respect to a dataset regarding X-ray imaging for pneumonia. We do this through using transfer learning models and demonstrate that pretrained models outperform models which are trained only on the dataset available. We also highlight additional methods which could be expanded upon in future research and hope to develop a state-of-the-art model for this particular dataset in the final version of this paper.