# Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach

arXiv: https://arxiv.org/abs/2301.02735 · HTML: https://mlsanity.com/paper/2301.02735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 6 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection](https://mlsanity.com/paper/1708.09839/) (2017)
- [Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images](https://mlsanity.com/paper/2004.05717/) (2020)
- [A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading](https://mlsanity.com/paper/1712.08107/) (2017)
- [Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach](https://mlsanity.com/paper/2011.14259/) (2020)
- [A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology](https://mlsanity.com/paper/1612.07180/) (2016)

## Abstract (arXiv)

COVID-19 has adversely affected humans and societies in different aspects. Numerous people have perished due to inaccurate COVID-19 identification and, consequently, a lack of appropriate medical treatment. Numerous solutions based on manual and automatic feature extraction techniques have been investigated to address this issue by researchers worldwide. Typically, automatic feature extraction methods, particularly deep learning models, necessitate a powerful hardware system to perform the necessary computations. Unfortunately, many institutions and societies cannot benefit from these advancements due to the prohibitively high cost of high-quality hardware equipment. As a result, this study focused on two primary goals: first, lowering the computational costs associated with running the proposed model on embedded devices, mobile devices, and conventional computers; and second, improving the model's performance in comparison to previously published methods (at least performs on par with state-of-the-art models) in order to ensure its performance and accuracy for the medical recognition task. This study used two neural networks to improve feature extraction from our dataset: VGG19 and ResNet50V2. Both of these networks are capable of providing semantic features from the nominated dataset. To this end, An alternative network was considered, namely MobileNetV2, which excels at extracting semantic features while requiring minimal computation on mobile and embedded devices. Knowledge distillation (KD) was used to transfer knowledge from the teacher network (concatenated ResNet50V2 and VGG19) to the student network (MobileNetV2) to improve MobileNetV2 performance and to achieve a robust and accurate model for the COVID-19 identification task from chest X-ray images.