A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection

Abstract

COVID-19 is a global pandemic that mostly affects patients' respiratory systems, and the only way to protect oneself against the virus at present moment is to diagnose the illness, isolate the patient, and provide immunization. In the present situation, the testing used to predict COVID-19 is inefficient and results in more false positives. This difficulty can be solved by developing a remote medical decision support system that detects illness using CT scans or X-ray images with less manual interaction and is less prone to errors. The state-of-art techniques mainly used complex deep learning architectures which are not quite effective when deployed in resource-constrained edge devices. To overcome this problem, a multi-objective Modified Heat Transfer Search (MOMHTS) optimized hybrid Random Forest Deep learning (HRFDL) classifier is proposed in this paper. The MOMHTS algorithm mainly optimizes the deep learning model in the HRFDL architecture by optimizing the hyperparameters associated with it to support the resource-constrained edge devices. To evaluate the efficiency of this technique, extensive experimentation is conducted on two real-time datasets namely the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets. The proposed methodology mainly offers increased speed for communication between the IoT devices and COVID-19 detection via the MOMHTS optimized HRFDL classifier is modified to support the resources which can only support minimal computation and handle minimum storage. The proposed methodology offers an accuracy of 99% for both the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets with minimal computational time, cost, and storage. Based on the simulation outcomes, we can conclude that the proposed methodology is an appropriate fit for edge computing detection to identify the COVID19 and pneumonia with higher detection accuracy.

Keywords: Cloud computing; Deep learning; Healthcare industry; Heat Transfer Search algorithm; Random forest; Web services.

Fig. 1

Outline of the proposed framework.

Fig. 2

Outline of the MOMHTS model.

Fig. 3

Proposed MOMHTS optimized.

Fig. 4

Samples images from the COVID19 lung CT scan dataset (a)-(b) Normal CT scan results, and (c)-(d) Abnormal CT scan results.

Fig. 5

Samples images from the Chest X-ray images (Pneumonia) dataset (a)-(b) Normal results, and (c)-(d) Abnormal results.

Fig. 6

Comparison in terms of latency.

Fig. 7

Comparative analysis using time complexity.

Fig. 8

Comparative analysis using F-measure.

Fig. 9

Comparative analysis using Specificity.

Fig. 10

ROC curve results. (a) Results for the Chest X-ray images (Pneumonia) dataset and (b) Results for the COVID19 lung CT scan dataset.

Fig. 11

Comparison in terms of application sizes.

Fig. 12

Computational complexity analysis using energy consumption for the proposed model with and without optimization.