WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

Alofi, Najla; Alonezi, Wafa; Alawad, Wedad

doi:10.3991/ijoe.v17i13.27373

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…DTL increases efficiency and unlabeled data. Fine-tuning or retraining these architectures for medical data is also a successful approach [16]- [18].…”

Section: Deep Transfer Learningmentioning

confidence: 99%

Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm

et al. 2023

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An infection caused by bacteria can lead to severe complications affecting bloodstream disease. At present, blood cultures are used to identify bacteria. However, blood culture is a time-consuming and labor-intensive method of diagnosing disease. The effect of delayed early diagnosis is that it influences the mortality risk. Thus, it is urgent to develop an initial prediction model to identify patients with bloodstream infections. This paper focused on classifying the bacteria using a deep learning approach. Besides, techniques of deep learning have the ability to enhance the bacterial classification process more effectively. Using the transfer learning-based convolutional neural network technique involved to develop our model. In addition, we compared the proposed model with another model used to find the best results. Compared to other models, the proposed model achieved an evaluation score with high accuracy of 98.62%. Medical decision-making may benefit from the proposed approach.

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“…DTL increases efficiency and unlabeled data. Fine-tuning or retraining these architectures for medical data is also a successful approach [16]- [18].…”

Section: Deep Transfer Learningmentioning

confidence: 99%

Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm

et al. 2023

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“…As explained in the previous paragraph and taking as a reference the article [45], was considered 80% images for the training process, leaving 20% images for the evaluation process of the trained modules of classification. Both partitions are duly represented by benign and malignant types of melanoma.…”

Section: 2mentioning

confidence: 99%

Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma Skin Cancer

et al. 2022

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In 2020, there were more than 1.2 million new skin cancer diagnoses, and melanoma was the most recurrent type of cancer. On the other hand, melanoma is the least common but most serious form of skin cancer affecting both men and women. This work aims to assemble classification models to detect a case of melanoma with high accuracy based on a Convolutional Neural Networks system. The methodology considers training 21 models for image classification, with the best assembly performance of EfficientNet and VGG-19 architectures, the data augmentation technique was used to the images to improve its performance. The results show 92.85% of accuracy, 71.50% of sensitivity, and 94.89% of specificity, with an improvement of 0.06% in accuracy and specificity. The assembly of the classification models achieved higher accuracy in melanoma skin cancer image classification.

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“…In the blood and bone marrow, basophils are one of the smallest WBC subsets, accounting for less than 2% of the total. Moreover, monocytes are a type of WBCs with nongranular cytoplasm that are formed in the bone marrow and account for 2%-10% of all WBCs [10][11][12]. T cells and B cells are the two types of lymphocytes, which are the smallest of the WBCs.…”

Section: Introductionmentioning

confidence: 99%

EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

SivaRao,

Rao

2023

Nano Biomedicine and Engineering

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In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques.

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WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

Cited by 3 publications

References 15 publications

Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm

Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm

Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma Skin Cancer

EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

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