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

Kasani, Payam Hosseinzadeh; Park, Sang-Won; Jang, Jae-Won

doi:10.3390/diagnostics10121064

Cited by 31 publications

(26 citation statements)

References 47 publications

(56 reference statements)

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“…This article compared the ViT-CNN ensemble model with Resnet50, Densenet121, and VGG16 three classic CNN models. Model in Literature [ 11 ]. Literature [ 11 ] has the best current research results on diagnosis of acute lymphoblastic leukemia.…”

Section: Resultsmentioning

confidence: 99%

“… Model in Literature [ 11 ]. Literature [ 11 ] has the best current research results on diagnosis of acute lymphoblastic leukemia. This article compared the accuracy of the model they proposed.…”

Section: Resultsmentioning

confidence: 99%

“…Model in Literature [ 11 ]. Literature [ 11 ] has the best current research results on diagnosis of acute lymphoblastic leukemia.…”

Section: Resultsmentioning

confidence: 99%

“…The accuracy rate of cell classification reached 85.8%, and the correct diagnosis rate for leukemia reached 94% [ 10 ]. Kasani et al combined two models of NASNetLarge and VGG19 to classify leukemic B-lymphoblast cells and normal B-lymphoid precursor cells with a classification accuracy rate of 96.58%, which can accurately diagnose acute lymphoblastic leukemia and proved that the ensemble model was more effective than a single network [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model

Jiang

Dong

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT-CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement-random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT-CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer-aided diagnosis of acute lymphoblastic leukemia.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…Model in Literature [ 11 ]. Literature [ 11 ] has the best current research results on diagnosis of acute lymphoblastic leukemia.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model

Jiang

Dong

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…The model achieves an accuracy of 98.6% for the dataset used. Kasani et al [16] propose to use a pretrained CNN model in an aggregated fashion to detect ALL from microscopic WBC images. The authors use several data augmentation techniques to avoid overfitting.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

Ramaneswaran

Srinivasan

Vincent

et al. 2021

Computational and Mathematical Methods in Medicine

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Acute lymphoblastic leukemia (ALL) is the most common type of pediatric malignancy which accounts for 25% of all pediatric cancers. It is a life-threatening disease which if left untreated can cause death within a few weeks. Many computerized methods have been proposed for the detection of ALL from microscopic cell images. In this paper, we propose a hybrid Inception v3 XGBoost model for the classification of acute lymphoblastic leukemia (ALL) from microscopic white blood cell images. In the proposed model, Inception v3 acts as the image feature extractor and the XGBoost model acts as the classification head. Experiments indicate that the proposed model performs better than the other methods identified in literature. The proposed hybrid model achieves a weighted F1 score of 0.986. Through experiments, we demonstrate that using an XGBoost classification head instead of a softmax classification head improves classification performance for this dataset for several different CNN backbones (feature extractors). We also visualize the attention map of the features extracted by Inception v3 to interpret the features learnt by the proposed model.

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Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI

Perumalraja,

Felcia Logan's Deshna,

Swetha

2024

Statistics in Medicine

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The growth of artificial intelligence (AI) in the healthcare industry tremendously increases the patient outcomes by reshaping the way we diagnose, treat and monitor patients. AI‐based innovation in healthcare include exploration of drugs, personalized medicine, clinical diagnosis investigations, robotic‐assisted surgery, verified prescriptions, pregnancy care for women, radiology, and reviewed patient information analytics. However, prediction of AI‐based solutions are depends mainly on the implementation of statistical algorithms and input data set. In this article, statistical performance review on various algorithms, Accuracy, Precision, Recall and F1‐Score used to predict the diagnosis of leukemia, glaucoma, and diabetes mellitus is presented. Review on statistical algorithms' performance, used for individual disease diagnosis gives a complete picture of various research efforts during the last two decades. At the end of statistical review on each disease diagnosis, we have discussed our inferences that will give future directions for the new researchers on selection of AI statistical algorithm as well as the input data set.

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

Cited by 31 publications

References 47 publications

Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model

Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model

Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI

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