An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

Abstract

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial intelligence (AI) techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images. However, the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs, which is not widely available in several countries. This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks (CNNs), which does not require a custom hardware to run compared to currently available CNN models. The proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU (0.54% of AlexNet parameters). This model is highly beneficial for countries where high-end GPUs are luxuries. Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96% accuracy.

Chest X-ray analysis empowered with deep learning: A systematic review

“…From the studies indicated in Table 7 it is visible that, 61.9% of the studies have achieved more than 95% accuracy. The models used in these studies are ResNet-50 [56] , VGG-16 [56] , [72] , VGG-19 [70] , CapsNet [88] , DensNet121 [75] , MobileNet [82] , MobileNetV2 [83] , InceptionV3 [83] , Xception [83] , InceptionResNetV2 [82] and CNNs developed from scratch [6] , [105] , [107] , [108] , [129] . From this analysis, it can be seen that among the studies that have achieved more than 95% accuracy values, only 38.5% of studies have come up with newly developed CNN architectures.…”

“…Only the study done by Bekhet et al. [129] has not used a GPU, since they mainly focused on building an efficient method that even can run on a normal CPU. Thus, it can be observed that many recent studies have used GPUs for their implementation.…”

“…Moreover, the average accuracy of the studies that have used Adam is 95.8%. Moreover, five studies out of the 13 studies that achieved more than 95% accuracy have used Adam optimizer [56] , [70] , [82] , [88] , [105] , while Momentum [72] , Adamax [75] and SGD [129] has been used by one study each. The rest have not mentioned an optimizer in their studies.…”

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

“…The COVID-19 pandemic continues to pose several challenges to medical systems worldwide, and the ability to make quick clinical choices is critical [ 48 ]. Predictive machine learning algorithms that analyze medical images and estimate risk are pretty valuable.…”

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

“… [36] , Alqudah, Qazan and Alqudah [37] , Chakraborty, Dhavale and Ingole [38] , Karakanis and Leontidis [39] , Bekhet et al. [40] ; Huang and Liao [41] developed lightweight CNNs using Chest X-ray images based COVID-19 detection. Zebin and Rezvy [42] , Nayak et al.…”