A Neural Network Architecture Using Separable Neural Networks for the Identification of 'Pneumonia' in Digital Chest Radiographs
| Autor: | K. Thirupathi Rao, N. Sarada |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of e-Collaboration. 17:89-100 |
| ISSN: | 1548-3681 1548-3673 |
| DOI: | 10.4018/ijec.2021010106 |
| Popis: | In recent years, convolutional neural networks had a wide impact in the fields of medical image processing. Image semantic segmentation and image classification have been the main challenges in this field. These two techniques have been seeing a lot of improvement in medical surgeries which are being carried out by robots and autonomous machines. This work will be working on a convolutional model to detect pneumonia in a given chest x-ray scan. In addition to the convolution model, the proposed model consists of deep separable convolution kernels which replace few convolutional layers; one main advantage is these take in a smaller number of parameters and filters. The described model will be more efficient, robust, and fine-tuned than previous models developed using convolutional neural networks. The authors also benchmarked the present model with the CheXnet model, which almost predicts over 16 abnormalities in the given chest-x-rays. |
| Databáze: | OpenAIRE |
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