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Workshop: Medical Imaging Meets NeurIPS

Modified VGG16 Network for Medical Image Analysis

Amulya Vatsavai


Thoracic diseases, like pneumonia and emphysema, affect millions of people around the globe every year. Chest radiography is essential to detecting and treating these diseases. Manually interpreting radiographical images is a time-consuming and fatiguing task. In regions without enough access to radiologists or radiographical equipment, the inability to analyze these images adversely affects patient care. Recent deep learning based thoracic disease classification using X-Ray images has been shown to perform on par with expert radiologists in interpreting medical images. The purpose of this study is to compare the transfer learning performance of different deep learning algorithms on their detection of thoracic pathologies in chest radiographs. In addition, we present a simple modification to the well-known VGG16 network to overcome overfitting. Comparative analysis shows that careful utilization of pretrained networks may provide a good alternative to specialized handcrafted networks due to the lack of sufficient labeled images in the medical domain.

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