Deep learning models for colorectal polyps
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2021-06-10
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MDPI AG
Resumen
Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date
Palabras clave
Colon cancer
Deep learning
Detection
Classification
Localization
CNN
Autoencoders
Deep learning
Detection
Classification
Localization
CNN
Autoencoders
Descripción
Materias
Cita
Bardhi, O., Sierra-Sosa, D., Garcia-Zapirain, B., & Bujanda, L. (2021). Deep learning models for colorectal polyps. Information (Switzerland), 12(6). https://doi.org/10.3390/INFO12060245