Abstract
The new strains of the pandemic COVID-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of COVID-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect COVID-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is .
Highlights
The pandemic COVID-19 is looming as a worst menace on the world populations while its several new strains are being identified
RT-PCR is still a gold standard for COVID-19 testing, but deep learning techniques to identify the virus from medical images can be helpful in certain circumstances, such as: unavailability of RT-PCR kits
We propose an interpretable deep learning model: pseudo prototypical part network (PsProtoPNet), and experiment it over the dataset of CT-scan images, see Section 2.4
Summary
The pandemic COVID-19 is looming as a worst menace on the world populations while its several new strains are being identified. RT-PCR is still a gold standard for COVID-19 testing, but deep learning techniques to identify the virus from medical images can be helpful in certain circumstances, such as: unavailability of RT-PCR kits. Many models have been proposed to detect COVID-19 from the medical images, see [3,4,5,6,7,8,9,10,11,12,13,14,15]. We propose an interpretable deep learning model: pseudo prototypical part network (PsProtoPNet), and experiment it over the dataset of CT-scan images, see Section 2.4. PsProtoPNet is closely related to ProtoPNet [16], Gen-ProtoPNet [17] and NP-Proto-PNet [18], but strikingly different from these models
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