The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods.
There are approximately 6.5 million patients in the U.S. suffering from chronic wounds and approximately 140,000 patients hospitalized every year with new wounds. With a long healing process, this demands the need for a non-contact, low cost, and remote monitoring solution that can assist clinicians in diagnosing and treating a patient's wound. This will reduce the burden of countless office visits, especially for those who are elderly and incapacitated. We present a mobile platform based wound 3D imaging app. The app is the only integrated measurement solution encompassing wound area and volume through low cost yet accurate 3D imaging. Extensive experiments show the app has 1.14% and 4.41% relative errors for wound area and volume measurement respectively, far exceeding currently employed clinic methods. In addition, non-invasive volume measurement methods currently use expensive industrial 3D (.$20K) cameras, but our solution provides cheap and accurate results.
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