The article by highlights that pre-processing can improve a system’s accuracy. With the use of Contrast-Limited Adaptive Histogram Equalization (CLAHE) and a Butterworth bandpass filter, the authors were able to enhance the contrast of X-ray images and eliminate the noise leading to an accuracy of 99.93%. Authors in proposed a novel, hybrid, multimodal deep learning system. Experiments using ReCoNet for differentiating COVID vs Pneumonia vs Normal were shown to have an accuracy > 97%. The main reason for using this preprocessor was to dynamically enhance the lung regions that are useful in detecting COVID-19. In the article by, the authors proposed the use of a multi-level CNN-based preprocessor. In addition, acquiring the medical image’s data and annotations can be extremely difficult and expensive. As mentioned in, medical image segmentation datasets suffer from scarce and weak annotations. The main obstacle in overcoming the segmentation problem is imperfect datasets. The authors in used a method based on U-NET and ResNet to perform the segmentation of CT images with an accuracy reaching 95%. In addition, improvements were made at each step of the workflow. Milestones in pre-processing, feature extraction, and assigning a classification were required to achieve the required results. The proposed ML-based method was able to classify chest X-rays into 3 classes: normal (healthy), COVID-19, and pneumonia, which can be similar to images of patients infected by COVID-19.Īs the COVID pandemic intensified, more investigators focused on automatic lung disease recognition. We conducted the study using a large public dataset. We tested 5 different pre-processing methods and investigated their effect on the final classification. In this article, we present the impact pre-processing can have on the results of a classification system. Thus, the implementation of AI and ML in COVID-19 and other lung diseases seems to be the desired natural progression. Artificial intelligence (AI) and ML can be used in numerous applications such as cybersecurity, pedestrian detection, telemedicine, biometrics or sports analytics. But, the use of machine learning (ML)-based methods can improve efficiency, support medics in the diagnosis of COVID-19, speed up the time to diagnosis, and lighten the already burdened health care system.Īt the same time, modern technologies have gathered more interest. However, X-ray analysis can be time-consuming and requires highly educated specialists to interpret. Moreover, CXR imaging is more widely available than CT imaging, especially in developing countries due to high equipment and maintenance costs. This imaging modality is highly available and accessible in many clinical locations, and it is considered standard equipment in most healthcare systems. In addition, patients suffering from COVID-19 can also present with abnormalities on chest X-ray images that are characteristic of infection. The RT-PCR test can detect SARS-CoV-2 ribonucleic acid (RNA) from respiratory specimens (collected through nasopharyngeal or oropharyngeal swabs). Currently, the main screening method for detecting COVID-19 infections is reverse transcriptase-polymerase chain reaction (RT-PCR) testing. Currently, people all over the world are doing their best to overcome the pandemic’s impact on the social, medical, psychologic, economic, and industrial aspects of society. It has caused societies to close, crowded streets to become deserted, pubs and clubs to be silenced, and popular meeting places to die down. The occurrence of the COVID-19 pandemic in 2020 has shaken up the modern world.
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