ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification

Verma, Ekansh; Singh, Vijendra

doi:10.1007/978-981-15-0798-4_14

Cited by 13 publications

(2 citation statements)

References 5 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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“…MobileNetV2 [ 28 ] is a convolutional neural network (CNN) architecture that comprises depthwise separable convolution, which splits the standard convolution operation into depthwise and pointwise convolutions. In the depthwise convolution, the same filter is applied to each input channel independently, reducing the computational cost.…”

Section: Proposed Deep Feature Selection Based Approachmentioning

confidence: 99%

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

et al. 2023

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Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.

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