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Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.

Climate is the absolute most occasions that influence the human life in each measurement, running from nourishment to fly while then again it is the most tragic wonders. In this manner, expectation of climate wonders is of significant enthusiasm for human culture to keep away from or limit the devastation of climate risks. Climate forecast is unpredictable because of clamor and missing qualities dataset. Various endeavors were made to make climate forecast as precise as would be prudent, yet at the same time the complexities of commotion are influencing exactness. In this paper, the five-year rainfall record of weather is used for predicting the rainfall by calculating the performance and accuracy through 10 cross-fold validation technique. Its initial step is gathering, isolating, sorting, and detachment of datasets dependent on future vectors. Arrangement strategy has numerous calculations, some of them are Support Vector Machine (SVM), Naive Bayes, Random Forest, and Decision Tree. Prior to the execution of each strategy, the model is made and afterward preparing of dataset has been made on that model. Learning the calculation created model must be fit for both the information dataset and estimate the records of class name. Various classifiers, for example, Linear SVM, Ensemble, Decision tree has been utilized and their precision and time broke down on the dataset. At last, all the calculation and results have been determined and analyzed in the terms of accuracy and execution time.

Classification is one of the most used machine learning technique especially in the prediction of daily life things. Its first step is grouping, dividing, categorizing, and separation of datasets based on future vectors. Classification procedure has many algorithms, some of them are Random Forest, Naive Bayes, Decision Tree and Support Vector Machine. Before the implementation of every technique, the model is created and then training of dataset has been made on that model. Learning the algorithm-generated model must be fit for both the input dataset and forecast the records of class label. Many models are available for prediction of a class label from unknown records. In this paper, different classifiers such as Linear SVM, Ensemble, the Decision tree has been applied and their accuracy and time analyzed on different datasets. The Liver Patient, Wine Quality, Breast Cancer and Bupa Liver Disorder datasets are used for calculating the performance and accuracy by using 10 cross-fold validation technique. In the end, all the applied algorithm results have been calculated and compared in the terms of accuracy and execution time.

Amyloidoma is a highly unusual presentation of amyloidosis in tumoral or nodular form. Isolated soft tissue amyloidomas in individuals with end-stage renal disease on chronic hemodialysis is exceedingly rare, particularly in the era of advanced dialysis technologies. We report the case of a 55-year-old male with end-stage renal disease due to autosomal-dominant polycystic kidney disease, on HD for over 30 years, who was found to have soft-tissue, dialysis-related (β2 -microglobulin) amyloidomas (DRA). He presented with painful, palpable masses within the thoracic and abdominal walls. Serum β2 -microglobulin level was only mildly elevated at 24.9 mg/L. Biopsy confirmed amyloidosis with positivity for Congo Red staining and apple-green birefringence under polarized light. Amyloid subtyping with immunohistochemistry showed positive β2 -microglobulin staining within the deposits. Conservative therapy involving pain management and close monitoring resulted in eventual improvement in symptoms and thus proved to be a viable option for treatment.

Serum sickness-like reaction is a rare immunological condition which may develop following exposure to certain drugs such as penicillins, cephalosporins, and trimethoprim-sulfamethoxazole, among many others. It is described classically as a type III hypersensitivity response to heterologous proteins. Its true mechanism is still unclear. We present a case of serum sickness-like reaction to clarithromycin, a commonly prescribed drug for the treatment of respiratory tract infections. The patient had been taking this drug for 3 days when she experienced generalized body aches, rash, arthralgia, and shortness of breath, prompting presentation to the emergency department. Laboratory studies showed decreased C4 and total complement with a slightly elevated sedimentation rate. After exclusion of other possible causes, the diagnosis of serum sickness-like reaction was made. The patient responded well to nonsteroidal antiinflammatory medication, antihistamines, and a short, tapering dose of steroids. To our knowledge, serum sickness-like reaction to clarithromycin has never been reported previously. This case emphasizes the need for increased clinical awareness of such an adverse outcome to clarithromycin use.