Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks

Shah, Salman; Nawaz, Wajahat; Jalil, Bushra; Khan, Hassan

doi:10.1007/978-981-15-0798-4_3

Cited by 21 publications

(12 citation statements)

References 15 publications

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“…SDCT-AuxNet [35] 0.948 Neighborhood-correction algorithm (NCA) [54] 0.910 Ensemble model based on MobileNetV2 [55] 0.894 Deep Multi-model Ensemble Network (DeepMEN) [50] 0.885 Ensemble CNN based on SENet and PNASNet [56] 0.879 Deep Bagging Ensemble Learning [57] 0.876 LSTM-DENSE [58] 0.866 Ensemble CNN model [59] 0.855 Multi-stream model [60] 0.848…”

Section: F1-scorementioning

confidence: 99%

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

et al. 2021

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Leukemia is a kind of blood cancer that influences people of all ages and is one of the leading causes of death worldwide. Acute lymphoblastic leukemia (ALL) is the most widely recognized type of leukemia found in the bone marrow of the human body. Traditional disease diagnostic techniques like blood and bone marrow examinations are slow and painful, resulting in the demand for non-invasive and fast methods. This work presents a non-invasive, convolutional neural network (CNN) based approach that utilizes medical images to perform the diagnosis task. The proposed solution consisting of a CNN-based model uses an attention module called Efficient Channel Attention (ECA) with the visual geometry group from oxford (VGG16) to extract better quality deep features from the image dataset, leading to better feature representation and better classification results. The proposed method shows that the ECA module helps to overcome morphological similarities between ALL cancer and healthy cell images. Various augmentation techniques are also employed to increase the quality and quantity of training data. We used the classification of normal vs. malignant cells (C-NMC) dataset and divided it into seven folds based on subject-level variability, which is usually ignored in previous methods. Experimental results show that our proposed CNN model can successfully extract deep features and achieved an accuracy of 91.1%. The obtained findings show that the proposed method may be utilized to diagnose ALL and would help pathologists.

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Section: F1-scorementioning

confidence: 99%

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

et al. 2021

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“…Shah et al [ 23 ] designed a custom-designed deep learning model by a combination of deep CNN and recurrent neural networks. The proposed ensemble model tackled the visual similarity between normal and malignant cells by extracting the spectral features using discrete cosine transform in conjunction with an recurrent neural network (RNN).…”

Section: Introductionmentioning

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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“…A custom model based on a fusion of CNN, LSTM was presented which used spectral features of cells by using discrete cosine transform in conjunction with an RNN to extract B-ALL image features. e method they used was an ensemble of convolutional and recurrent neural networks that used the AlexNet and DenseNet pretrained networks [35]. One of the most important techniques that have been highly considered by researchers to solve this challenge is the use of a set of ensemble techniques, given the nature of these techniques that employ common attributes of algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…is study has been published in the MedRxiv database at (https://www.medrxiv.org/content/10.1101/2021.07.10. 21260312v1) [35]. As this research was conducted in line with an international challenge, the authors are presenting it on MedRxiv database to register the idea.…”

Section: Data Availabilitymentioning

confidence: 99%

Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Ghaderzadeh

Hosseini

Asadi

et al. 2022

Scientific Programming

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Introduction. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia, a deadly white blood cell disease that impacts the human bone marrow. ALL detection in its early stages has always been riddled with complexity and difficulty. Peripheral blood smear (PBS) examination, a common method applied at the outset of ALL diagnosis, is a time-consuming and tedious process that largely depends on the specialist’s experience. Materials and Methods. Herein, a fast, efficient, and comprehensive model based on deep learning (DL) was proposed by implementing eight well-known convolutional neural network (CNN) models for feature extraction on all images and classification of B-ALL lymphoblast and normal cells. After evaluating their performance, four best-performing CNN models were selected to compose an ensemble classifier by combining each classifier’s pretrained model capabilities. Results. Due to the close similarity of the nuclei of cancerous and normal cells, CNN models alone had low sensitivity and poor performance in diagnosing these two classes. The proposed model based on the majority voting technique was adopted to combine the CNN models. The resulting model achieved a sensitivity of 99.4, specificity of 96.7, AUC of 98.3, and accuracy of 98.5. Conclusion. In classifying cancerous blood cells from normal cells, the proposed method can achieve high accuracy without the operator’s intervention in cell feature determination. It can thus be recommended as an extraordinary tool for the analysis of blood samples in digital laboratory equipment to assist laboratory specialists.

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Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks

Cited by 21 publications

References 15 publications

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

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

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

Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

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