DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification

Xiao, Fenrui; Kuang, Ruifeng; Ou, Zhonghong; Xiong, Baiqiao

doi:10.1007/978-981-15-0798-4_9

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…use a lot of convolution layers, whereas our objective was to create a simple deep learning model for ALL classification. Secondly, the VGG16 model has a higher feature extraction ability for the classification of ALL cell images as shown in [50]. The shallow network keeps more information about the underlying features, which is important for cell texture identification.…”

Section: Eca-net Based On Vgg16mentioning

confidence: 99%

“…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%

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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: Eca-net Based On Vgg16mentioning

confidence: 99%

Section: F1-scorementioning

confidence: 99%

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

et al. 2021

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“…We present an effective method for accurate, automatic recognition of ALL cells from blood smear microscopic images and perform extensive experiments on the C-NMC dataset to investigate its effectiveness. The C-NMC dataset attains WF1S values of approximately

, allowing these cells to be identified with very high accuracy, which is superior to other sophisticated classifiers in the literature [ 32 , 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Weakly Supervised Ternary Stream Data Augmentation Fine-Grained Classification Network for Identifying Acute Lymphoblastic Leukemia

et al. 2021

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Due to the high incidence of acute lymphoblastic leukemia (ALL) worldwide as well as its rapid and fatal progression, timely microscopy screening of peripheral blood smears is essential for the rapid diagnosis of ALL. However, screening manually is time-consuming and tedious and may lead to missed or misdiagnosis due to subjective bias; on the other hand, artificially intelligent diagnostic algorithms are constrained by the limited sample size of the data and are prone to overfitting, resulting in limited applications. Conventional data augmentation is commonly adopted to expand the amount of training data, avoid overfitting, and improve the performance of deep models. However, in practical applications, random data augmentation, such as random image cropping or erasing, is difficult to realistically occur in specific tasks and may instead introduce tremendous background noises that modify actual distribution of data, thereby degrading model performance. In this paper, to assist in the early and accurate diagnosis of acute lymphoblastic leukemia, we present a ternary stream-driven weakly supervised data augmentation classification network (WT-DFN) to identify lymphoblasts in a fine-grained scale using microscopic images of peripheral blood smears. Concretely, for each training image, we first generate attention maps to represent the distinguishable part of the target by weakly supervised learning. Then, guided by these attention maps, we produce the other two streams via attention cropping and attention erasing to obtain the fine-grained distinctive features. The proposed WT-DFN improves the classification accuracy of the model from two aspects: (1) in the images can be seen details since cropping attention regions provide the accurate location of the object, which ensures our model looks at the object closer and discovers certain detailed features; (2) images can be seen more since erasing attention mechanism forces the model to extract more discriminative parts’ features. Validation suggests that the proposed method is capable of addressing the high intraclass variances located in lymphocyte classes, as well as the low interclass variances between lymphoblasts and other normal or reactive lymphocytes. The proposed method yields the best performance on the public dataset and the real clinical dataset among competitive methods.

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“…Although many articles have already been published, there is still room for performance improvements with better genericity of the trained model. Moreover, the CNN-based approaches experience data insufficiency to avoid overfitting, where the en-semble of different CNN architectures relieves the data scarcity limitations, as demonstrated in various articles [10,20,41,50,66,83]. With the aforementioned thing in mind, this article intends to contribute to the exploration of building a robust ensemble model for the ALL classification, incorporating different pre-processing.…”

Section: Contributionsmentioning

confidence: 99%

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

Mondal¹,

Hasan²,

Jawad³

et al. 2021

Preprint

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Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by numerous immature lymphocytes. Even though automation in ALL prognosis is an essential aspect of cancer diagnosis, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands experienced pathologists to carefully read the cell images, which is arduous, time-consuming, and often suffers inter-observer variations. This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of different deep CNNs to recommend a better ALL cell classifier.The weights for the ensemble candidate models are estimated from their corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We utilize the publicly available C-NMC-2019 ALL dataset to conduct all the comprehensive experiments. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, has outputted a weighted F1-score of 88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, 1

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DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification

Cited by 10 publications

References 17 publications

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

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

Weakly Supervised Ternary Stream Data Augmentation Fine-Grained Classification Network for Identifying Acute Lymphoblastic Leukemia

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

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