Related references
Note: Only part of the references are listed.Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study
Mustafa Ghaderzadeh et al.
JOURNAL OF MEDICAL INTERNET RESEARCH (2021)
Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review
Mustafa Ghaderzadeh et al.
SCIENTIFIC PROGRAMMING (2021)
X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic
Mustafa Ghaderzadeh et al.
BIOMED RESEARCH INTERNATIONAL (2021)
Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia
Segu Praveena et al.
BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK (2020)
Leukemia incidence trends at the global, regional, and national level between 1990 and 2017
Ying Dong et al.
EXPERIMENTAL HEMATOLOGY & ONCOLOGY (2020)
Accurate automatic detection of acute lymphatic leukemia using a refined simple classification
F. E. Al-Tahhan et al.
MICROSCOPY RESEARCH AND TECHNIQUE (2020)
Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning
Ehsan Fathi et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE (2020)
Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
Alexandra Bodzas et al.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2020)
LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks
Haoyi Fan et al.
JOURNAL OF BIOPHOTONICS (2019)
Acute lymphoblastic leukemia segmentation using local pixel information
Saif S. Al-jaboriy et al.
PATTERN RECOGNITION LETTERS (2019)
Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis
Laura Boldu et al.
JOURNAL OF CLINICAL PATHOLOGY (2019)
Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification
Luis H. S. Vogado et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)
Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks
Sarmad Shafique et al.
TECHNOLOGY IN CANCER RESEARCH & TREATMENT (2018)
A novel segmentation algorithm for nucleus in white blood cells based on low-rank representation
Feilong Cao et al.
NEURAL COMPUTING & APPLICATIONS (2017)
Densely Connected Convolutional Networks
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
Xception: Deep Learning with Depthwise Separable Convolutions
Francois Chollet
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)
A new thresholding technique based on fuzzy set as an application to leukocyte nucleus segmentation
V. P. Ananthi et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)
Benign hematogone-rich lymphoid proliferations can be distinguished from B-lineage acute lymphoblastic leukemia by integration of morphology, immunophenotype, adhesion molecule expression, and architectural features
LM Rimsza et al.
AMERICAN JOURNAL OF CLINICAL PATHOLOGY (2000)