scholarly journals Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

2018 ◽  
Vol 17 ◽  
pp. 153303381880278 ◽  
Author(s):  
Sarmad Shafique ◽  
Samabia Tehsin
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Data Augmentation ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Automated Detection ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Different Color

Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, we deployed pretrained AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. We also compared the data sets with different color models to check the performance over different color images. For acute lymphoblastic leukemia detection, we achieved a sensitivity of 100%, specificity of 98.11%, and accuracy of 99.50%; and for acute lymphoblastic leukemia subtype classification the sensitivity was 96.74%, specificity was 99.03%, and accuracy was 96.06%. Unlike the standard methods, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

Classification of Microchannel Flame Regimes Based on Convolutional Neural Networks

2021 ◽  
Author(s):  
Seyed Navid Roohani Isfahani ◽  
Vinicius M. Sauer ◽  
Ingmar Schoegl
Keyword(s):  
Data Augmentation ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Data Sets ◽  
Data Set ◽  
Testing Data ◽  
Experimental Approaches ◽  
Transition Points ◽  
Combustion Regimes

Abstract Micro-combustion has shown significant potential to study and characterize the combustion behavior of hydrocarbon fuels. Among several experimental approaches based on this method, the most prominent one employs an externally heated micro-channel. Three distinct combustion regimes are reported for this device namely, weak flames, flames with repetitive extinction and ignition (FREI), and normal flames, which are formed at low, moderate, and high flow rate ranges, respectively. Within each flame regime, noticeable differences exist in both shape and luminosity where transition points can be used to obtain insights into fuel characteristics. In this study, flame images are obtained using a monochrome camera equipped with a 430 nm bandpass filter to capture the chemiluminescence signal emitted by the flame. Sequences of conventional flame photographs are taken during the experiment, which are computationally merged to generate high dynamic range (HDR) images. In a highly diluted fuel/oxidizer mixture, it is observed that FREI disappear and are replaced by a gradual and direct transition between weak and normal flames which makes it hard to identify different combustion regimes. To resolve the issue, a convolutional neural network (CNN) is introduced to classify the flame regime. The accuracy of the model is calculated to be 99.34, 99.66, and 99.83% for “training”, “validation”, and “testing” data-sets, respectively. This level of accuracy is achieved by conducting a grid search to acquire optimized parameters for CNN. Furthermore, a data augmentation technique based on different experimental scenarios is used to generate flame images to increase the size of the data-set.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Single-Cell Developmental Classification of B-Cell Precursor Acute Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse

2018 ◽  
Vol 64 ◽  
pp. S33-S34 ◽  
Author(s):  
Kara Davis ◽  
Zinaida Good ◽  
Jolanda Sarno ◽  
Astraea Jager ◽  
Nikolay Samusik ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Single Cell ◽  
Lymphoblastic Leukemia ◽  
Cell Precursor

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew II: Cone Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Powdery Mildew ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Model Construction ◽  
Data Sets ◽  
Data Set ◽  
Sampling Plans ◽  
Simulated Sampling

Sequential sampling models for estimation and classification of the incidence of powdery mildew (caused by Podosphaera macularis) on hop (Humulus lupulus) cones were developed using parameter estimates of the binary power law derived from the analysis of 221 transect data sets (model construction data set) collected from 41 hop yards sampled in Oregon and Washington from 2000 to 2005. Stop lines, models that determine when sufficient information has been collected to estimate mean disease incidence and stop sampling, for sequential estimation were validated by bootstrap simulation using a subset of 21 model construction data sets and simulated sampling of an additional 13 model construction data sets. Achieved coefficient of variation (C) approached the prespecified C as the estimated disease incidence, [Formula: see text], increased, although achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.03 with the number of sampling units evaluated in this study. The 95% confidence interval of the median difference between [Formula: see text] of each yard (achieved by sequential sampling) and the true p of the original data set included 0 for all 21 data sets evaluated at levels of C of 0.1 and 0.2. For sequential classification, operating characteristic (OC) and average sample number (ASN) curves of the sequential sampling plans obtained by bootstrap analysis and simulated sampling were similar to the OC and ASN values determined by Monte Carlo simulation. Correct decisions of whether disease incidence was above or below prespecified thresholds (pt) were made for 84.6 or 100% of the data sets during simulated sampling when stop lines were determined assuming a binomial or beta-binomial distribution of disease incidence, respectively. However, the higher proportion of correct decisions obtained by assuming a beta-binomial distribution of disease incidence required, on average, sampling 3.9 more plants per sampling round to classify disease incidence compared with the binomial distribution. Use of these sequential sampling plans may aid growers in deciding the order in which to harvest hop yards to minimize the risk of a condition called “cone early maturity” caused by late-season infection of cones by P. macularis. Also, sequential sampling could aid in research efforts, such as efficacy trials, where many hop cones are assessed to determine disease incidence.

scholarly journals Acute Lymphoblastic Leukemia, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology

2021 ◽  
Vol 19 (9) ◽  
pp. 1079-1109
Author(s):  
Patrick A. Brown ◽  
Bijal Shah ◽  
Anjali Advani ◽  
Patricia Aoun ◽  
Michael W. Boyer ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Supportive Care ◽  
Lymphoblastic Leukemia ◽  
Philadelphia Chromosome ◽  
Treatment Strategies ◽  
Adolescent And Young Adult ◽  
Cancer Center ◽  
Nccn Guidelines ◽  
Treatment Regimens

The NCCN Guidelines for Acute Lymphoblastic Leukemia (ALL) focus on the classification of ALL subtypes based on immunophenotype and cytogenetic/molecular markers; risk assessment and stratification for risk-adapted therapy; treatment strategies for Philadelphia chromosome (Ph)-positive and Ph-negative ALL for both adolescent and young adult and adult patients; and supportive care considerations. Given the complexity of ALL treatment regimens and the required supportive care measures, the NCCN ALL Panel recommends that patients be treated at a specialized cancer center with expertise in the management of ALL This portion of the Guidelines focuses on the management of Ph-positive and Ph-negative ALL in adolescents and young adults, and management in relapsed settings.

scholarly journals Study of Some Biochemical Parameters in Iraqi Children with Acute Lymphoblastic Leukemia

2015 ◽  
Vol 12 (2) ◽  
pp. 371-378
Author(s):  
Baghdad Science Journal
Keyword(s):  
Bone Marrow ◽  
Acute Lymphoblastic Leukemia ◽  
Biochemical Parameters ◽  
Serum Proteins ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Total Serum ◽  
Childhood All ◽  
Marrow Cavity ◽  
Increased Risk

Leukemia or cancer of the blood is the most common childhood cancer, Acute lymphoblastic leukemia (ALL), is the most common form of leukemia that occurs in children. It is characterized by the presence of too many immature white blood cells in the child’s blood and bone marrow, Acute lymphoblastic leukemia can occur in adults too, treatment is different for children. Children with ALL develop symptoms related to infiltration of blasts in the bone marrow, lymphoid system, and extramedullary sites, such as the central nervous system (CNS). Common constitutional indications consist of fatigue (50%), pallor (25%), fever (60%), and weight loss (26%). Infiltration of blast cells in the marrow cavity and periosteum often lead to bone pain (23%) and disturbance of normal hematopoiesis. Thrombocytopenia with platelet counts less than 100,000 are seen in approximately 75% of patients. About 40% of patients with childhood ALL present with hemoglobin levels less than 7 g/dL. Although leukocyte counts greater than 50,000/mm3 occur in 20% of cases, neutropenia defined as an absolute neutrophil count less than 500 is common at presentation and is associated with an increased risk of infection. The aim of this study was to investigate the differentiations in some biochemical parameters (Hb, PCV, total serum proteins Aspartate amino transferase(AST), Alanin amino transferase (ALT), and Malondialdehyde (MDA) in blood which can be conceder as a marker of ALL. Samples were collected from 50 patients (between 1-16 years old) diagnosed with ALL after one month treatment with induction therapy, compared with 30 control samples taken from healthy persons at the same age . The ALT and MDA showed a significant increase p < 0.001 and p

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