scholarly journals An Efficient Approach for Segmentation and Classification of Acute Lymphoblastic Leukemia via Optimized Spike-based Network

2021 ◽  
Author(s):  
Ali Noshad ◽  
saeed fallahi
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Cells ◽  
Lymphoblastic Leukemia ◽  
Gaussian Function ◽  
Principal Component ◽  
Complex Issue ◽  
Morphological Variations ◽  
Learning Techniques ◽  
Proposed Model

Abstract Identification of uncontrolled accumulation of abnormal blood cells ( lymphoblasts ) considered to be a challenging task. Despite a wide variety of image processing and deep learning techniques, the task of extracting the features from Acute Lymphoblastic Leukemia (ALL) images and detection of ALL cells is still challenging and complex issue due to morphological variations in cells. In order to overcome these drawbacks, in this study, we proposed a new framework with a combination of spiking and residual network for the detection and classification of lymphoblasts cells from healthy ones in blood sample images. According to this, features are extracted using a novel First-Spike-based approach, and then the Gaussian function is applied to remove the low-intensity edges. To reduce dimensionality, Principal Component Analysis ( PCA ) is used and finally, a developed deep residual architecture is employed to diagnose the ALL blood cells from the reconstructed images. To show the effectiveness of the proposed model, it is evaluated on microscopic images of blood samples from ALL Images (ALL- IDB ) and ISBI -2019 C- NMC dataset. The results show the superiority of the model to be an appropriate choice for future biomedical imaging tasks.

scholarly journals Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
S. Ramaneswaran ◽  
Kathiravan Srinivasan ◽  
P. M. Durai Raj Vincent ◽  
Chuan-Yu Chang
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Classification Performance ◽  
Image Feature ◽  
Pediatric Malignancy ◽  
Proposed Model ◽  
Feature Extractor ◽  
Life Threatening ◽  
Better Than

Acute lymphoblastic leukemia (ALL) is the most common type of pediatric malignancy which accounts for 25% of all pediatric cancers. It is a life-threatening disease which if left untreated can cause death within a few weeks. Many computerized methods have been proposed for the detection of ALL from microscopic cell images. In this paper, we propose a hybrid Inception v3 XGBoost model for the classification of acute lymphoblastic leukemia (ALL) from microscopic white blood cell images. In the proposed model, Inception v3 acts as the image feature extractor and the XGBoost model acts as the classification head. Experiments indicate that the proposed model performs better than the other methods identified in literature. The proposed hybrid model achieves a weighted F1 score of 0.986. Through experiments, we demonstrate that using an XGBoost classification head instead of a softmax classification head improves classification performance for this dataset for several different CNN backbones (feature extractors). We also visualize the attention map of the features extracted by Inception v3 to interpret the features learnt by the proposed model.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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 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 Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

Acute Leukemia

2016 ◽  
Author(s):  
Richard A. Larson ◽  
Roland B Walter
Keyword(s):  
Acute Myeloid Leukemia ◽  
Acute Lymphoblastic Leukemia ◽  
Acute Leukemia ◽  
Myeloid Leukemia ◽  
Who Classification ◽  
Lymphoblastic Leukemia ◽  
World Health ◽  
Acute Leukemias ◽  
Acute Myeloid

The acute leukemias are malignant clonal disorders characterized by aberrant differentiation and proliferation of transformed hematopoietic progenitor cells. These cells accumulate within the bone marrow and lead to suppression of the production of normal blood cells, with resulting symptoms from varying degrees of anemia, neutropenia, and thrombocytopenia or from infiltration into tissues. They are currently classified by their presumed cell of origin, although the field is moving rapidly to genetic subclassification. This review covers epidemiology; etiology; classification of leukemia by morphology, immunophenotyping, and cytogenetic/molecular abnormalities; cytogenetics of acute leukemia; general principles of therapy; acute myeloid leukemia; acute lymphoblastic leukemia; and future possibilities. The figure shows the incidence of acute leukemias in the United States. Tables list World Health Organization (WHO) classification of acute myeloid leukemia and related neoplasms, expression of cell surface and cytoplasmic markers for the diagnosis of acute myeloid leukemia and mixed-phenotype acute leukemia, WHO classification of acute lymphoblastic leukemia, WHO classification of acute leukemias of ambiguous lineage, WHO classification of myelodysplastic syndromes, European LeukemiaNet cytogenetic and molecular genetic subsets in acute myeloid leukemia with prognostic importance, cytogenetic and molecular subtypes of acute lymphoblastic leukemia, terminology used in leukemia treatment, and treatment outcome for adults with acute leukemia. This review contains 1 highly rendered figure, 9 tables, and 117 references.

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