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. 2022 Nov 5;12(11):2700.
doi: 10.3390/diagnostics12112700.

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Madhusree Kuanr  1 , Puspanjali Mohapatra  1 , Sanchi Mittal  1 , Mahesh Maindarkar  2 , Mostafa M Fauda  3 , Luca Saba  4 , Sanjay Saxena  1 , Jasjit S Suri  2   5
Affiliations

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Madhusree Kuanr et al. Diagnostics (Basel). .

Abstract

Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

Keywords: CNN model; COVID-19; Maxwell–Boltzmann similarity; ResNet-50; feature extraction; recommender system.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Evolution of an X-ray image for a COVID-19 patient over days (1 (A), 3 (B), 6 (C), 7 (D), 8 (E), and 10 (F), respectively) [11] (reproduced with permission).
Figure 2
Figure 2
The global treatment recommender system for COVID-19.
Figure 3
Figure 3
Various types of recommender systems.
Figure 4
Figure 4
The basic architecture of convolutional neural network [22] (reproduced with permission).
Figure 5
Figure 5
The basic architecture of ResNet.
Figure 6
Figure 6
ResNet with residual block.
Figure 7
Figure 7
The basic architecture of VGG Net [73] (reproduced with permission); CONV: convolution layer and FC: fully connected network.
Figure 8
Figure 8
The architecture or local system for the proposed system.
Figure 9
Figure 9
Phase-1 of the proposed system.
Figure 10
Figure 10
The data flow of the proposed system.
Figure 11
Figure 11
Performance evaluation of the fine-tuned CNN models.
Figure 12
Figure 12
Graph for mean/standard deviation of AHS.
Figure 13
Figure 13
Graph for a mean of AHS.
Figure 14
Figure 14
Mean average precision @k graphs for DFTV datasets for k = 5 and k = 10.
Figure 15
Figure 15
Mean Average precision @k graphs for DFCV datasets for k = 5 and k = 10.
Figure 16
Figure 16
ROC curves for the performance analysis of CNN models.
Figure 17
Figure 17
Graphs for average running time comparison.
Figure 18
Figure 18
Top 10 similar images for recommendation.
See this image and copyright information in PMC

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