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

Jiang, Zhencun; Dong, Zhicheng; Wang, Lingyang; Jiang, Wenping

doi:10.1155/2021/7529893

Cited by 52 publications

(21 citation statements)

References 26 publications

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“…The mode discussed here has a larger batch size and is trained with more iterations. Research material [ 15 ] was applied to a vision transformer, but the proposed model was evaluated with explainable AI, which is more reliable. The groundwork [ 16 ] chose the BigDL method, but any evaluation matrix was missing.…”

Section: Results and Analysismentioning

confidence: 99%

“…They also discuss the advantages and disadvantages of each method. Jiang et al have proposed [ 15 ] the ViT-CNN ensemble model to help diagnose ALL by classifying cancerous and normal cells. The ViT-CNN ensemble model extracts features from cell pictures in two alternative ways to improve classification, resulting in a very accurate detection method with 99.03% accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Abir

Uddin

Khanam

et al. 2022

Computational Intelligence and Neuroscience

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White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as “defender cells.” But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians’ workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation’s shortcomings in artificial intelligence (AI) models’ decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Abir

Uddin

Khanam

et al. 2022

Computational Intelligence and Neuroscience

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“…Jiang et al presented a method called the ViT-CNN ensemble model for automatic ALL classification [18]. The model was designed using Vision Transformer (ViT) and CNN.…”

Section: Previous Related Workmentioning

confidence: 99%

Classification of Acute Lymphocytic Leukemia (ALL) using Ventral-Dorsal and bottleneck Attention-based Network(VDBAN)

Zolfaghari

Sajedi

2022

Preprint

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One of the diseases with a high mortality rate worldwide is leukemia or blood cancer. Therefore, a timely and reliable diagnosis of leukemia is essential. Today, Artificial Intelligence and Deep Learning methods can automatically, quickly, and cost-effectively identify many diseases. In this paper, the state-of-the-art Convolutional Neural Network (CNN)based on the proposed attention blocks is introduced for the automatic classification of Acute Lymphoblastic Leukemia (ALL) and normal (healthy) cell images. Before sending images to the network, the preprocessing operation, including data augmentation and White Blood Cells (WBCs, or Leuko cytes) segmentation by Circular Hough Transform (CHT), is performed. Then, the proposed network attention blocks are designed and implemented based on the ventral and dorsal pathways of the occipital lobe that process visual information in the brain. The proposed network is ResNet18, in which the VentralDorsal Attention Block (VDAB) is embedded inside its blocks and the Bottleneck Attention Block (BAB) between them. Therefore, it is called Ventral-Dorsal and Bottleneck Attention Network (VDBAN). We designed three networks, including the first model (ResNet18), the second model (ResNet18 + VDAB) and the proposed model ((ResNet18+ VDAB + BAB) or VDBAN), and trained and tested them with similar parameters on the ALL-IDB2 database. The simulation results showed that the proposed model had obtained the highest value in all performance metrics compared to the other two models in the test step.

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“…Chen et al [96] employed a Transformer-CNN-based architecture to classify gastric histopathology WSIs in a binary setting (i.e., normal against abnormal). Jiang et al [97] addressed the diagnosis of acute lymphocytic leukemia through the classification of leukemic B-lymphoblast cells (i.e., cancer cells) and B-lymphoid precursors (i.e., normal cells) with a Transformer-CNN ensemble, and a data enhancement method that tackles the problem of class imbalance. Zheng et al [98] proposed a novel graph-based Vision Transformer architecture to classify lung WSIs (i.e., adenocarcinoma, squamous cell carcinoma, and normal histology).…”

Section: ) Whole Slide Image Classificationmentioning

confidence: 99%

A Survey on Attention Mechanisms for Medical Applications: are we Moving Toward Better Algorithms?

et al. 2022

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The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning methods (including Transformers) for several medical applications based on the types of tasks that may integrate several works pipelines of the medical domain. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.

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

Cited by 52 publications

References 26 publications

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Classification of Acute Lymphocytic Leukemia (ALL) using Ventral-Dorsal and bottleneck Attention-based Network(VDBAN)

A Survey on Attention Mechanisms for Medical Applications: are we Moving Toward Better Algorithms?

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