Symptoms of the COVID-19 vary from person to person however, the most frequently reported symptoms include fatigue, coughing, and shortness of breath. 2022) and use the segmented images for specific purposes like predicting the type of the fetal brain (pathological or neurotypical) and predicting the gestational age of the fetus (Gangopadhyay et al. The use of machine learning techniques in the medical field was not limited to diagnosing diseases only, but also included several domains such as segmenting the medical images (Pal et al. 2019), brain tumors (Salçin 2019), pneumonia (Avsar 2021), lung diseases (Kabiraj 2022) and lung cancer (Gunjan et al. Among the many other studies based on medical images, there are different applications such as detection and diagnosis of gastric cancer (Cao et al.
#Xray pneumonia skin#
Ensembles of convolutional neural networks (CNN) has shown to be an efficient tool for skin cancer detection (Al-Karawi 2022) while segmentation of skin diseases also possible with the methods based on CNN (Huang et al. ( 2021) proposed an optimized technique for identification of blindness in retinal images using the deep learning models. In general, these techniques have proven to be effective in diagnosing the diseases with acceptable accuracy and high speed.
#Xray pneumonia how to#
( 2022) discussed the prospects of supervised machine learning (SML) in the healthcare sector, the challenges it faces, how to solve it and the opportunity for healthcare through AI and SML in the near future. In recent years, the reliance on machine learning techniques in the medical field has increased dramatically. On the other hand, the widespread usage of rapid diagnosis tools, which help in taking measurements and suggesting an appropriate treatment, is an evidence of both sieging effect of the pandemic and their usefulness in mitigating the spread of virus. The huge number of infected people and insufficient number of medical staff and health facilities in some countries increased the burden on the health system.
#Xray pneumonia manual#
The flare-up of the COVID-19 has increased the need for new effective and faster diagnostic methods than those manual diagnosis provided by the experts. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy. In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. The results showed that the proposed model with low number of computational layers outperforms the pre-trained benchmark models, achieving an accuracy value of 89.89% while the best pre-trained model (Efficient-Net B2) achieved accuracy of 85.7%.
The performance of the proposed model was compared with those obtained by applying transfer learning to the widely used convolutional neural network models. To evaluate the model performance, accuracy, precision, recall, and F1-Score were calculated. The proposed model is designed to classify the X-ray images belonging to one of the four classes that are Healthy, COVID-19, viral pneumonia, and bacterial pneumonia. To address these problems, this study aims to design a lightweight differential diagnosis model based on convolutional neural networks. However, the widely used convolutional neural network architectures typically have many layers causing them to be computationally expensive. Usage of deep learning models is known to be helpful for automated diagnosis of COVID-19 from the X-ray images. The manual examination of these X-ray images may cause problems especially when there is lack of medical staff.
X-ray is a useful imaging modality widely utilized for diagnosing COVID-19 virus that infected a high number of people all around the world.
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