Tuberculosis (TB) is among top ten causes of deaths worldwide. It is the single most cause of deaths by an infectious disease and is ranked 2nd only after the HIV/AIDS. In third world countries, the diagnosis of TB is done through conventional methods. To diagnostic results are obtain from conventional methods such as blood, culture, sputum and biopsies. They are tedious as well as take long time like 1-2 weeks or maybe evenmore. Therefore, to lower the detection time and raise the accuracy of diagnosis several researches have been carried out. In the past fifty years, due to the advanced and sophisticated technologies, in medical as well as computer science fields, have paved a way to utilize the essence of both the areas. In Artificial Intelligence (AI) various Machine Learning (ML) algorithms have furthered the interests in Computer-aided Detection (CADe) and Diagnosis (CADx) methods. These methodologies assist in medical field for diagnosing the diseases through clinical signs and symptoms as well as radiological images of the patient. They have been implemented for the diagnosis of TB. Advances in AI algorithms, has unveiled great promises in identifying the presence and absence of TB. As of late, many attempts have been made to formulate the strategies to increase the classification accuracy of TB diagnosis using the AI and machine learning approach. This review paper, aims to describes the diverse AI approaches employed in the diagnosis of TB.
Tuberculosis (TB) is airborne infectious disease which has claimed many lives than any other infectious disease. Chest X-rays (CXRs) are often used in recognizing TB manifestation site in chest. Lately, CXRs are taken in digital formats, which has made a huge impact in rapid diagnosis using automated systems in medical field. In our current work, four simple Convolutional Neural Networks (CNN) models such as VGG-16, VGG-19, RestNet50, and GoogLenet are implemented in identification of TB manifested CXRs. Two public TB image datasets were utilized to conduct this research. This study was carried out to explore the limit of accuracies and AUCs acquired by simple and small-scale CNN with complex and large-scale CNN models. The results achieved from this work are compared with results of two previous studies. The results indicate that our proposed VGG-16 model has gained highest score overall compared to the models from other two previous studies.
A highly stereoselective total synthesis of attenols A and B is described. The salient features of this synthesis are the utilization of a reductive radical cyclization strategy for methyl center creation, a Prins cyclization/reductive opening cascade for anti‐1,3‐diol motif generation, and a double alkylation tosylmethyl isocyanide (TosMIC) strategy to construct the spiro acetal segment.
B i s m u t h ( I I I ) T r i f l a t e C a t a l y z e d C o n d e n s a t i o n o f I s a t i n w i t h I n d o l e s a n d P y r r o l e s Abstract: Indoles and pyrroles undergo a rapid condensation with isatin in the presence of 2 mol% of Bi(OTf) 3 , under mild reaction conditions, to afford the corresponding 3,3-di(3-indolyl)-and 3,3-di(2-pyrroryl)oxindoles in excellent yields and high regioselectivity. This method is ideal for the direct introduction of indoles and pyrroles onto an isatin moiety at the 3-position.
Arylamines undergo smooth cyclization with 2-deoxy-D-ribose on the surface of montmorillonite KSF clay under mild conditions to afford the corresponding sugar-derived chiral tetrahydroquinolines in high yields with moderate diastereoselectivity. The assignment of the stereochemistry of the product was achieved by various NMR studies.
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