A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

Kassani, Sara Hosseinzadeh; Kassani, Peyman Hosseinzadeh; Wesolowski, Michal J.; Schneider, Kevin A.; Deters, Ralph

doi:10.1109/ictc46691.2019.8939959

Cited by 47 publications

(26 citation statements)

References 23 publications

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“…VGG-16 has a better feature learning ability than ResNet-50 because it is deeper than ResNet-50, and it can get more sparse features [ 12 ]. A proposed deep learning-based hybrid approach, which is enriched by complex data augmentation procedures, is able to elicit high-level features from input images [ 19 ]. Experimental results confirm that the developed model yields better prediction than individual models [ 8 , 20 ] for leukemic B-lymphoblast classification with 96.17% overall accuracy.…”

Section: Discussionmentioning

confidence: 99%

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

Rezayi

Mohammadzadeh

Bouraghi

et al. 2021

Computational Intelligence and Neuroscience

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Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2 ∗ 2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis.

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Section: Discussionmentioning

confidence: 99%

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

Rezayi

Mohammadzadeh

Bouraghi

et al. 2021

Computational Intelligence and Neuroscience

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“…They used fine-tuning and transfer learning to propose an ensemble model based on VGG19 and NasNetLarge architecture. Kassani et al [37] proposed a hybrid model based on VGG16 and MobileNet for the classification of ALL cell images. Global average pooling (GAP) was used between VGG16 and MobileNet to extract high-level features from ALL cell images.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…NASNet-Large with VGG19 [36] 0.965 2020 Hybrid model (VGG16 + MobileNet) [37] 0.961 2019 LeukoNet [38] 0.896 2018 Proposed Method 0.911 2021 Table 8. Methods with the top entry of C-NMC 2019 Challenge.…”

Section: Accuracy Yearmentioning

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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“…eir proposed algorithm shows outstanding results of 99.03% which proves the effectiveness of the proposed method as a CAD system for Acute Lymphoblastic Leukemia (ALL). Kassani et al designed a hybrid approach in which VGG16 and MobileNet are combined to extract the deep features followed by classification of Leukemic Blymphoblast [55]. eir proposed approach is enriched with various data augmentation methods and attained 96.17% accuracy, 95.17% sensitivity, and 98.58% specificity.…”

Section: Hybrid Deep Learning Approachesmentioning

confidence: 99%

A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning

Bukhari

Yasmin

Sammad

et al. 2022

Mathematical Problems in Engineering

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Leukemia is a fatal category of cancer-related disease that affects individuals of all ages, including children and adults, and is a significant cause of death worldwide. Particularly, it is associated with White Blood Cells (WBC), which is accompanied by a rise in the number of immature lymphocytes and cause damage to the bone marrow and/or blood. Therefore, a rapid and reliable cancer diagnosis is a critical requirement for successful therapy to raise survival rates. Currently, a manual analysis of blood samples obtained through microscopic images is done to diagnose this disease, which is often very slow, time-consuming, and less accurate. Furthermore, in microscopic analysis, the appearance and shape of leukemic cells seem very similar to normal cells which make detection more difficult. In the past decades, deep learning utilizing Convolutional Neural Networks (CNN) has provided state-of-the-art approaches for image classification problems; however, there is still a gap to improve their efficacy, learning procedure, and performance. Therefore, in this research study, we proposed a new variant of deep learning algorithm to diagnose leukemia disease by analyzing the microscopic images of blood samples. The proposed deep learning architecture emphasizes the channel associations on all levels of feature representation by incorporating the squeeze and excitation learning that recursively performs recalibration on channel-wise feature outputs by modeling channel interdependencies explicitly. In addition, the incorporation of the squeeze-and-excitation process enhances the feature discriminability of leukemic and normal cells, and strategically assists in exposing informative features of leukemia cells while suppressing less valuable ones as well as improving feature representational power of deep learning algorithm. We show that piling these learning operations of squeeze and excite together in a deep learning model can improve the performance of the model in diagnosing leukemia from microscopic images based on blood samples of patients. Furthermore, an extensive set of experiments are performed on both cropped cells and full-size microscopic images as well as with data augmentation to address the problem of fewer data and to further boost their performance. The proposed model is tested on two publicly available datasets of blood samples of leukemia patients, namely, ALL_IDB1 and ALL_IDB2. The suggested deep learning model exhibits good results and can be utilized to make a reliable computer-aided diagnosis for leukemia cancer.

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A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

Cited by 47 publications

References 23 publications

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

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

A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning

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