Convolutional neural network for diagnosis of viral pneumonia and COVID‐19 alike diseases

Abstract

Reverse‐Transcription Polymerase Chain Reaction (RT‐PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID‐19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x‐ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X‐ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross‐validation was carried out. The performance of the model using overall dataset was compared with the means of cross‐validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough

“…Most of the work in this field has focused on diagnosis in the formal diagnosis stage when medical inspections have been performed. For example, Umar Ibrahim et al ( 4 ) proposed a bidirectional adversarial network-based framework to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia using CT images. Wang et al ( 5 ) developed a fully automated DL pipeline for the visualization of lesions and the diagnosis of pneumonia caused by COVID-19 on CXR images that was able to discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia.…”

“…In the convolution stage (Figure 2), the parameters of the pretrained language model were transferred to the disease prediction model through fine-tuning. We set the kernel size as [3,4,5] for the disease prediction model. To avoid information loss, the width of the convolution kernel was set as the same as the dimension of the word vector, and each kernel had 128 output channels.…”

“…Recent research on this subject has focused on the use of artificial intelligence (AI) to aid the diagnosis of respiratory diseases (4)(5)(6)(7)(8), especially the diagnosis of coronavirus disease 2019 . However, most studies investigated intelligent diagnosis assistance during the formal diagnostic phase when medical examinations have been performed.…”

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

“…In order to address these issues, scientists merge radiographic imaging with computer applications to developed computer aided diagnosis which allow screening of thousand images with high accuracy, precision and specificity [8][9]. CAD has shown to aid medical experts in the past for the detection of different types of cancer such as breast cancer [10], colon cancer [11], prostate cancer [12], tuberculosis [13], bacterial pneumonia [14], non-COVID-19 viral pneumonia [15], skin disease [16].…”

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review

“…In order to address these issues, scientists merge radiographic imaging with computer applications to develop computer-aided diagnosis (CAD), which allows screening of thousands of images with high accuracy, precision, and specificity [ 10 , 11 ]. CAD has been shown to aid medical experts in the past in the detection of different types of cancer, such as breast cancer [ 12 ], colon cancer [ 13 ], prostate cancer [ 14 ], brain cancer [ 15 ], tuberculosis [ 16 ], bacterial pneumonia [ 17 ], non-COVID-19 viral pneumonia [ 18 ], and skin diseases [ 19 ].…”