scholarly journals Automated Detection of White Blood Cells Cancer Diseases

2019 ◽  
Vol 8 (6S) ◽  
pp. 366-369
Keyword(s):  
Side Effects ◽  
Random Forest ◽  
Blood Cells ◽  
White Blood Cells ◽  
Random Forest Classifier ◽  
Automated Detection ◽  
Learning Approach ◽  
Malignant Growth ◽  
Cancer Diseases

Mechanized analysis of white platelets malignant growth infections, for example, Leukemia and Myeloma is a difficult biomed-ical inquire about point. Our methodology introduces out of the blue another best in class application that helps with diagnosing the white platelets infections. we break these sicknesses into two classifications, every classification contains like side effects infections that may confound in diagnosing. In light of the specialist's determination, one of two methodologies is actualized. Each methodology is connected on one of the two maladies classification by processing distinctive highlights. At last, Random Forest classifier is connected for ultimate choice. The proposed methodology means to early disclosure of white platelets malignancy, decrease the misdiagnosis cases notwithstanding improve the framework learning approach. In addition, permitting the specialists just to have the last tuning on the outcome acquired from the framework. The proposed methodology accomplished an exactness of 93% in the principal classification and 95% in the second class.

scholarly journals Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

2018 ◽  
Vol 611 ◽  
pp. A97 ◽  
Author(s):  
J. Pasquet-Itam ◽  
J. Pasquet
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Sloan Digital Sky Survey ◽  
Light Curves ◽  
Support Vector ◽  
Learning Approach ◽  
Photometric Redshifts ◽  
K Nearest Neighbors ◽  
Sky Survey ◽  
Extraction Step

We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope.

scholarly journals Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables

2020 ◽  
Vol 17 (24) ◽  
pp. 9378
Author(s):  
Amy Marie Campbell ◽  
Marie-Fanny Racault ◽  
Stephen Goult ◽  
Angus Laurenson
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Land Surface ◽  
Environmental Changes ◽  
Random Forest Classifier ◽  
Sea Surface Salinity ◽  
Learning Approach ◽  
Climate Variables ◽  
Surface Salinity ◽  
Machine Learning Approach

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.

Segmentation of Overlapping Red Blood Cells for Malaria Blood Smear Images by U-Net Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 2126-2129
Author(s):  
Fatih Veysel Nurçin ◽  
Elbrus Imanov
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Similarity Index ◽  
Cell Segmentation ◽  
Learning Approach ◽  
Automated Segmentation ◽  
Jaccard Similarity

Automated segmentation of red blood cells is a widely applied task in order to evaluate red blood cells for certain diseases. Counting of malaria parasites requires individual red blood cell segmentation in order to evaluate the severity of infection. For such an evaluation, correct segmentation of red blood cells is required. However, it is a difficult task due to the presence of overlapping red blood cells. Existing methodologies employ preprocessing steps in order to segment red blood cells. We propose a deep learning approach that has a U-Net architecture to provide fully automated segmentation of red blood cells without any initial preprocessing. While red blood cells were segmented, irrelevant objects such as white blood cells, platelets and artifacts were removed. The network was trained and tested on 5600 and 600 samples respectively. Segmentation of overlapping red blood cells was achieved with 93.8% Jaccard similarity index. To the best of our knowledge, our results surpassed previous outcomes.

scholarly journals Correlation of Granulocyte Mobilization with Etiocholanolone and the Subsequent Development of Myelosuppression in Patients with Acute Leukemia Receiving Therapy

Blood ◽  
1968 ◽  
Vol 31 (5) ◽  
pp. 580-588 ◽  
Author(s):  
HERMAN A. GODWIN ◽  
THEODORE S. ZIMMERMAN ◽  
HARRY R. KIMBALL ◽  
SHELDON M. WOLFF ◽  
SEYMOUR PERRY
Keyword(s):  
Bone Marrow ◽  
Side Effects ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Subsequent Development ◽  
Negative Response ◽  
Positive Test ◽  
Hematologic Toxicity ◽  
Test Response

Abstract 1. Etiocholanolone was employed in the assessment of bone marrow granulocyte reserves in a group of patients with acute leukemia prior to the administration of therapy. 2. Normal levels of circulating white blood cells and granulocytes and/or remission bone marrow status were often associated with abnormal test responses. 3. Individuals having a positive test response experienced significantly less hematologic toxicity following therapy than did those patients having a negative response. 4. Etiocholanolone proved to be a safe agent without significant side effects. 5. This test can be helpful in the prediction of toxicity following antileukemic therapy.

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