The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.
Some specific kinds of proteins are responsible for the risk of immediate type I allergic reaction. Therefore, the proteins that are made to use in the consumer product should be checked for their allergic reactions before introducing them in the market. The FAO/WHO instructions for the assessment of allergic proteins depend on the linear sequence window identity and short peptide hits misclassify many proteins as allergen proteins. This study introduces the AllerPredictor model that predicts the allergen & non-allergen proteins depending on the sequence of proteins. Data was downloaded from two major databases, FARRP and UniProtKB. The results of this model were validated with the help of self-consistency testing, independence testing, and jackknife testing. The accuracy for self-consistency validation is 99.89%, for the independence testing is 74.23%, and for 10-fold cross-validation, it is 97.17%. To predict the allergen and non-allergen proteins, this AllerPredictor model has a better accuracy than other existing methods.
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