Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

By Suat Toraman, Talha Burak Alakus, Ibrahim Turkoglu. Chaos, Solitons & Fractals, 2020

577 citations recorded by the source corpus.

Highlights • In this study, unlike CNN architectures, COVID-19 was determined from chest X-ray images with a smaller number of layers.• More COVID-19, pneumonia, and no-findings images were used than in previous studies. This increases the reliability of the system more.• As is known, reducing the size of the image may cause some information in the image to be lost. Given these facts, good classification accuracy has been achieved with capsule networks, even the image size has been reduced to 128 × 128 pixels.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/dbf7814b781458a7a9f3df5e09bbd5695d9991fc

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Machine Learning: Algorithms, Real-World Applications and Research Directions (2021, background): “Deep learning also provides exciting solutions to the problems of medical image processing and is seen as a crucial technique for potential applications, particularly for COVID19 pandemic [10, 78, 111].”
Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network (2020, methodology): “[46] nCOVnet Chest X-ray 337 — total; 192 — COVID-19; 142- Normal Binary Random sampling 70% for training and 30% for testing [47] CapsNet Chest X-ray 3150 — total; 1050 — Normal; 1050 — Pneumonia; 1050 — COVID-19 Binary and Multi-class 10-fold cross-validation”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “Since the time series used in this study are also nonlinear and irregular, so it can be said that the results of this study are consistent with previous studies.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, background): “As in many areas, the most preferred deep learning model in medicine is Convolu-tional Neural Networks (CNN)-based deep learning models [20].”