scholarly journals Correction: Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms

The Analyst ◽  
2020 ◽  
Vol 145 (22) ◽  
pp. 7447-7447
Author(s):  
Adam H. Agbaria ◽  
Guy Beck ◽  
Itshak Lapidot ◽  
Daniel H. Rich ◽  
Joseph Kapelushnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Cells ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Infrared Microscopy

Correction for ‘Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms’ by Adam H. Agbaria et al., Analyst, 2020, DOI: 10.1039/D0AN00752H.

Diagnosis of inaccessible infections using infrared microscopy of white blood cells and machine learning algorithms

The Analyst ◽  
2020 ◽  
Vol 145 (21) ◽  
pp. 6955-6967
Author(s):  
Adam H. Agbaria ◽  
Guy Beck ◽  
Itshak Lapidot ◽  
Daniel H. Rich ◽  
Joseph Kapelushnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ftir Spectroscopy ◽  
Blood Cells ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Infrared Microscopy

Rapid and objective diagnosis of the etiology of inaccessible infections by analyzing WBCs spectra, measured by FTIR spectroscopy, using machine-learning.

scholarly journals Detection of Blood Cancer Cells Using Microscopic Images

2021 ◽  
Vol 9 (8) ◽  
pp. 995-1003
Author(s):  
Vidyashree M S
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Blood Cancer ◽  
Acute Myeloid

Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is use for segmentation and clustering. Support vector machine algorithm is used for classification. Keywords: k-means, Support vector machine, Lymphoma, Acute Lymphocytic Lymphoma, Machine Learning

Detection of Extended-Spectrum β-Lactamase-ProducingEscherichia coliUsing Infrared Microscopy and Machine-Learning Algorithms

2019 ◽  
Vol 91 (3) ◽  
pp. 2525-2530 ◽  
Author(s):  
Uraib Sharaha ◽  
Eladio Rodriguez-Diaz ◽  
Orli Sagi ◽  
Klaris Riesenberg ◽  
Itshak Lapidot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Infrared Microscopy ◽  
Extended Spectrum

scholarly journals IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Nighat Bibi ◽  
Misba Sikandar ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Sikandar Ali
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Network Resources ◽  
And Performance

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients’ lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

Machine Learning Approaches to Define Candidates for Ambulatory Single Level Laminectomy Surgery

2021 ◽  
pp. 219256822097983
Author(s):  
Qiyi Li ◽  
Haoyan Zhong ◽  
Federico P. Girardi ◽  
Jashvant Poeran ◽  
Lauren A. Wilson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Value ◽  
Learning Algorithms ◽  
White Blood Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Improvement Program ◽  
Single Level ◽  
Laboratory Values ◽  
Same Day Discharge

Study Design: retrospective cohort study. Objectives: To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery. Methods: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures. Results: Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase. Conclusion: Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design.

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