Stain Color Normalization and Segmentation of Plasma Cells in Microscopic Images as a Prelude to Development of Computer Assisted Automated Disease Diagnostic Tool in Multiple Myeloma

Gupta, Ritu; Mallick, Pramit; Duggal, Rahul; Gupta, Anubha; Sharma, Ojaswa

doi:10.1016/j.clml.2017.03.178

Cited by 42 publications

(28 citation statements)

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“…This study was mainly based on a dataset of classification of normal versus malignant cells in B-ALL white blood cancer microscopic images (ISBI 2019) provided by SBI-Lab [ 11 , 12 , 43 , 44 ], which is available to the public at [ 21 ]. The goal of this challenge was to develop a machine learning solution for distinguishing normal cells from leukemic blast (malignant cells) in microscopic images of blood smears.…”

Section: Resultsmentioning

confidence: 99%

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

2020

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Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.

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Section: Resultsmentioning

confidence: 99%

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

2020

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“…To overcome the limitation of using a single dataset and to broaden the scope of our work, we extended our study to a second, independent and more recent dataset, C-NMC [30][31][32] . This dataset was used for the B-ALL normal versus malignant cell classification challenge at IEEE ISBI-2019 and consists of a large number of labeled images of normal and malignant cells.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…This dataset was used for the B-ALL normal versus malignant cell classification challenge at IEEE ISBI-2019 and consists of a large number of labeled images of normal and malignant cells. The cell images were extracted from blood smear microscopy images after normalizing the stain, as described in [30][31][32] . The total size of the training dataset is 10,661 images from 76 subjects.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Sahlol

Kollmannsberger

Ewees

2020

Sci Rep

202

101

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White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.

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“…2.2) in order to get rid of most black background. Staining and illumination error were presented in the images due to image capture with real clinical situations although reduced by stain normalization techniques as shown in [3,5]. The initial dataset was then split into original training set (see Sec.…”

Section: Details Of Initial Datasetmentioning

confidence: 99%

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Liu¹,

Long²

2019

Preprint

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Acute lymphoblastic leukemia (ALL) is a blood cancer which leads 111,000 depth globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle above challenges. The weighted F1-scores of preliminary test set and final test are 0.84 and 0.88 respectively employing our ensemble model predictions and ranked within top 10% in ISBI-2019 Classification of Normal vs. Malignant White Blood Cancer Cells contest. Our results preliminarily show the efficacy and accuracy of employing DL based techniques in ALL cells image analysis.

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Stain Color Normalization and Segmentation of Plasma Cells in Microscopic Images as a Prelude to Development of Computer Assisted Automated Disease Diagnostic Tool in Multiple Myeloma

Cited by 42 publications

References 0 publications

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

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