Computer Aided System (CAS) Of Lymphoblast Classification For Acute Lymphoblastic Leukemia (ALL) Detection Using Various Pre-Trained Models

Safuan, Syadia Nabilah Mohd; Tomari, Mohd Razali Md; Zakaria, Wan Nurshazwani Wan; Othman, Norzila; Suriani, Nor Surayahani

doi:10.1109/scored50371.2020.9251000

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…The submitted model has a shallow model loss. The study [ 12 ] has a model accuracy of 88.69% using histopathological transfer learning, and the study paper [ 13 ] had a batch size of 10, which is much lower than usual with only 6 epochs. The mode discussed here has a larger batch size and is trained with more iterations.…”

Section: Results and Analysismentioning

confidence: 99%

“…On a histopathology database, CNN is trained before being fine-tuned on the ALL database to recognize lymphoblast tissue types with an accuracy rate of 88.69%. Safuan et al [ 13 ] classified the WBC types to identify ALL with CNN, where pretrained models of DL like AlexNet, GoogLeNet, and VGG-16 are differentiated from each other to find the model that can classify better with a classification accuracy rate of 96.15%. Shafique and Tehsin [ 14 ] compared the different methods for the early detection of ALL.…”

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

View full text Add to dashboard Cite

show abstract

“…Before being optimized on the ALL database, CNN underwent training on histopathology databases to identify the various lymphoblast tissues and their types, having an accuracy rating of 88.69%. A CNN was used by [21] for classifying different WBC types to detect ALL, and previously trained DL frameworks, like AlexNet, GoogleNet, and VGG-16, were contrasted against one another to determine the model that could classify the most accurately, having a 96.15% accuracy rate. A technique [22] for leukemia detection was developed utilizing the Apache Spark BigDL package and the CNN framework for GoogleNet deep transfer learning.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder

Alshahrani,

Sharma,

Anand

et al. 2023

Life

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Bone marrow (BM) is an essential part of the hematopoietic system, which generates all of the body’s blood cells and maintains the body’s overall health and immune system. The classification of bone marrow cells is pivotal in both clinical and research settings because many hematological diseases, such as leukemia, myelodysplastic syndromes, and anemias, are diagnosed based on specific abnormalities in the number, type, or morphology of bone marrow cells. There is a requirement for developing a robust deep-learning algorithm to diagnose bone marrow cells to keep a close check on them. This study proposes a framework for categorizing bone marrow cells into seven classes. In the proposed framework, five transfer learning models—DenseNet121, EfficientNetB5, ResNet50, Xception, and MobileNetV2—are implemented into the bone marrow dataset to classify them into seven classes. The best-performing DenseNet121 model was fine-tuned by adding one batch-normalization layer, one dropout layer, and two dense layers. The proposed fine-tuned DenseNet121 model was optimized using several optimizers, such as AdaGrad, AdaDelta, Adamax, RMSprop, and SGD, along with different batch sizes of 16, 32, 64, and 128. The fine-tuned DenseNet121 model was integrated with an attention mechanism to improve its performance by allowing the model to focus on the most relevant features or regions of the image, which can be particularly beneficial in medical imaging, where certain regions might have critical diagnostic information. The proposed fine-tuned and integrated DenseNet121 achieved the highest accuracy, with a training success rate of 99.97% and a testing success rate of 97.01%. The key hyperparameters, such as batch size, number of epochs, and different optimizers, were all considered for optimizing these pre-trained models to select the best model. This study will help in medical research to effectively classify the BM cells to prevent diseases like leukemia.

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“…There are four different kinds of leukemia. Children have a greater probability to be diagnosed with acute lymphoblastic leukemia (ALL) [3], but adults are more likely to get acute myelogenous leukemia (AML), which is not hereditary. Both are generated by an excess production of WBC's.…”

Section: Introductionmentioning

confidence: 99%

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

Ghongade,

Reddy,

Gavini

et al. 2023

IJEEI

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White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non-leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction.

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Computer Aided System (CAS) Of Lymphoblast Classification For Acute Lymphoblastic Leukemia (ALL) Detection Using Various Pre-Trained Models

Cited by 8 publications

References 12 publications

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

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