4.7 Article

Assessing the impact of data augmentation and a combination of CNNs on leukemia classification

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Biomedical

Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques

Arjun Abhishek et al.

Summary: This paper presents a novel dataset with normal, Acute Myeloid Leukemia, and Acute Lymphoblastic Leukemia images, including close to 1700 cancerous blood cells. The dataset is enlarged by adding images from other sources to create a heterogeneous dataset used for automated classification tasks.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Chemistry, Analytical

Intelligent Medical IoT-Enabled Automated Microscopic Image Diagnosis of Acute Blood Cancers

Mohamed Esmail Karar et al.

Summary: This study proposes a new intelligent Internet of Medical Things (IoMT) framework for automated classification of acute leukemias using microscopic blood images. The framework collects blood samples using wireless digital microscopy, automatically identifies the blood conditions using a developed generative adversarial network (GAN) classifier, and sends the results to a hematologist for approval. The developed GAN classifier achieved high accuracy scores in binary and multi-class classifications when compared to existing methods.

SENSORS (2022)

Article Computer Science, Artificial Intelligence

An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia

Pradeep Kumar Das et al.

Summary: This paper proposes an efficient deep CNN framework for the diagnosis of Acute Lymphoblastic Leukemia (ALL), which utilizes a novel probability-based weight factor, resulting in improved performance. The proposed method is validated on public benchmark datasets, demonstrating the best accuracy in comparison to recent transfer learning-based techniques.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Chemistry, Multidisciplinary

An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification

Muhammad Zakir Ullah et al.

Summary: Leukemia is a common blood cancer that requires fast and painless diagnostic methods. This study proposes a method for leukemia diagnosis using medical images, showing an accuracy of 91.1%.

APPLIED SCIENCES-BASEL (2021)

Article Chemistry, Analytical

Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model

Luis Vogado et al.

Summary: This article introduces a convolutional neural network named LeukNet, trained on 18 image datasets with an accuracy of 98.61%. Cross-dataset experiments show that our method has better generalization ability and outperforms current state-of-the-art methods.

SENSORS (2021)

Review Engineering, Biomedical

A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

K. K. Anilkumar et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2020)

Article Health Care Sciences & Services

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

Nighat Bibi et al.

JOURNAL OF HEALTHCARE ENGINEERING (2020)

Article Biotechnology & Applied Microbiology

Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm

Satvik Dasariraju et al.

BIOENGINEERING-BASEL (2020)

Article Computer Science, Interdisciplinary Applications

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Mohamed Loey et al.

COMPUTERS (2020)

Article Computer Science, Information Systems

HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach

Kamran Kowsari et al.

INFORMATION (2020)

Article Multidisciplinary Sciences

Reconciling modern machine-learning practice and the classical bias-variance trade-off

Mikhail Belkin et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Mathematical & Computational Biology

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images

Tatdow Pansombut et al.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2019)

Article Medicine, General & Internal

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Nizar Ahmed et al.

DIAGNOSTICS (2019)

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

Article Engineering, Biomedical

Classification of acute leukemia using medical-knowledge-based morphology and CD marker

Jakkrich Laosai et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2018)

Article Automation & Control Systems

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)

Article Computer Science, Artificial Intelligence

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Ke Gu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Engineering, Biomedical

Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia

Jyoti Rawat et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2017)

Article Engineering, Biomedical

Automatic recognition of five types of white blood cells in peripheral blood

Seyed Hamid Rezatofighi et al.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2011)

Article Pathology

Normal structure, function, and histology of the bone marrow

Gregory S. Travlos

TOXICOLOGIC PATHOLOGY (2006)