Leukemia Blood Cell Image Classification Using Convolutional Neural Network

Thanh, Tran Thang; Vununu, Caleb; Atoev, Sukhrob; Lee, Suk-Hwan; Kwon, Ki-Ryong

doi:10.7763/ijcte.2018.v10.1198

Cited by 114 publications

(67 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With an average accuracy of 88.5%, the obtained results confirmed the superiority of using a CNN. Thanh et al [6] proposed a CNN-based method to differentiate between normal and abnormal blood cell images to detect leukemia at an early stage. Evaluated with a dataset of 1188 images, the proposed method achieved 96.6% classification accuracy.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

“…Thanh et al [6] 1188 CNN FC 96.6% Vogado et al [10] 377 CNN SVM 99% Rehman et al [25] 330 CNN FC 97.78% Shafique and Tehsin [26] 260 CNN FC 96.06% Pansombut et al [12] 363 CNN FC 80% Proposed work 2820 CNN FC 100%…”

Section: Number Of Images Methodology Accuracy Feature Extraction Clamentioning

confidence: 99%

“…This section covers some of the research conducted in the field of diseases, especially blood disease detection and diagnosis. Some of these studies pertain to traditional methods [8,9], which consist of several steps such as pre-processing, segmentation, feature extraction, and classification, whereas other methods pertain to deep-learning-based methods [6,[10][11][12], which use deep neural networks for end-to-end learning tasks.…”

Section: Related Studiesmentioning

confidence: 99%

“…The pooling layers control overfitting by reducing the number of features resulting from the convolutional layers. The fully connected layer gathers all the features of the descriptors that are to be classified by the last layer [6].…”

Section: Relu (X) = Max (0 X)mentioning

confidence: 99%

“…A 2012 study showed that about 352,000 adults and children all over the world develop leukemia, which starts in the bone marrow and is distinguished by the number of white cells increasing in an abnormal manner [1]. This disease has several causes, such as exposure to radiation and certain chemicals, as well as family history [6]. Diagnoses can be performed via a variety of tests, such as physical examination, blood test, blood count, and bone marrow biopsy.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

2020

View full text Add to dashboard Cite

Leukemia is a fatal disease that threatens the lives of many patients. Early detection can effectively improve its rate of remission. This paper proposes two automated classification models based on blood microscopic images to detect leukemia by employing transfer learning, rather than traditional approaches that have several disadvantages. In the first model, blood microscopic images are pre-processed; then, features are extracted by a pre-trained deep convolutional neural network named AlexNet, which makes classifications according to numerous well-known classifiers. In the second model, after pre-processing the images, AlexNet is fine-tuned for both feature extraction and classification. Experiments were conducted on a dataset consisting of 2820 images confirming that the second model performs better than the first because of 100% classification accuracy.Computers 2020, 9, 29 2 of 12 and classification. However, the success of each step depends on the success of the preceding step. For example, the success of classification depends on the success of the preceding feature extraction, which itself depends on the success of the preceding segmentation. Hence, high classification accuracy requires the success of all steps, each of which is non-trivial and problem-dependent [1,7].Recently, deep learning achieved many breakthroughs in different fields such as computer vision, natural language processing, and object recognition. Deep neural networks, such as convolutional neural networks (CNNs), can be used effectively to build computer-aided diagnostic systems. However, the design and training of deep neural networks are non-trivial and time-consuming tasks. Hence, instead of building a deep neural network from scratch, we use the concept of transfer learning, in which a deep network that achieved success in solving a certain problem is tuned to solve another problem.This paper proposes two classification models that are based on transfer learning and can distinguish between healthy and unhealthy blood smear images with high accuracy. These models employ AlexNet, which is a deep CNN that achieved huge success in the image classification challenge, ImageNet 2012.The remaining sections of this paper are arranged as follows: Section 2 covers some of the traditional and end-to-end-based methods described in the literature. Section 3 presents our two proposed classification models. Section 4 discusses their implementation in our experiments. Section 5 discusses the results obtained. Finally, Section 6 concludes the paper.

show abstract

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Section: Number Of Images Methodology Accuracy Feature Extraction Clamentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Relu (X) = Max (0 X)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

2020

View full text Add to dashboard Cite

show abstract

Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data

Ganesh,

Sivakumar,

Shankar

2024

Development of 6G Networks and Technology

View full text Add to dashboard Cite

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

et al. 2023

View full text Add to dashboard Cite

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for White Blood Cell (WBC) Counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter‐ and intraobserver variability. The present work introduces an image combined‐descriptor to detect blasts and determine their probable lineage. This strategy uses an Intra‐nucleus Mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner‐nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically‐segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co‐occurrence matrix representation. Both InMoP and Haralick‐based descriptors are calculated using the b‐channel from Lab color‐space. The combined‐descriptor is assessed by differentiating blasts from non‐leukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database‐D1 (n=260) is composed of healthy and Acute Lymphoid Leukemia (ALL) single cell images, and second database‐D2 contains Acute Myeloid Leukemia (AML) blasts (n=3,294) and non‐blast (n=15,071) cell images. In a first experiment, blasts vs non‐blast differentiation is performed by training with a subset of D2 (n=6,588) and testing in D1 (n=260), obtaining a training AUC of 0.991±0.002 and AUC=0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state‐of‐the‐art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from non‐blast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1‐score.This article is protected by copyright. All rights reserved.

show abstract

Leukemia Blood Cell Image Classification Using Convolutional Neural Network

Cited by 114 publications

References 4 publications

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Deep Transfer Learning in Diagnosing Leukemia in Blood Cells

Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

Contact Info

Product

Resources

About