Leukocytes Image Classification Using Optimized Convolutional Neural Networks

Hosseini, Maryam Sadat; Bani-Hani, Dana; Lam, Sarah S.

doi:10.1016/j.eswa.2022.117672

Cited by 16 publications

(5 citation statements)

References 54 publications

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“…Önerilen yöntem ile elde edilen parametre sayısına en yakın 126,000 parametre ile Hosseini vd. [33] ve 133,000 parametre ile Banik vd. [34] yöntemlerinde elde edilmiştir.…”

Section: Deneysel Sonuçlar (Experimental Results)unclassified

Classification of White Blood Cells using the Squeeze-Excitation Residual Network

FIRAT

2023

Bilişim Teknolojileri Dergisi

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Beyaz kan hücreleri, vücudun parazitler, bakteriler, virüsler gibi mikroorganizmalara karşı korunmasında etkin rol oynayan bağışıklık sisteminin önemli bir bileşenidir. Beyaz kan hücrelerinin yapısal özellikleri, alt türlerinin şekilleri ve sayıları insan sağlığı hakkında önemli bilgiler verebilmektedir. Hastalık teşhisinde doğru beyaz kan hücre tespiti klinik olarak oldukça önemlidir. Bu yüzden, doğru beyaz kan hücre sınıflandırma yöntemi kritik öneme sahiptir. Bu çalışmada, beyaz kan hücre sınıflandırması için Evrişimsel sinir ağı (ESA) tabanlı bir yöntem önerilmiştir. Önerilen yöntem sıkma-uyarma ağı ile artık ağ mimarisinin birleşiminden oluşan hibrit bir yöntemdir. Derin ağ mimarilerinde katman sayısı arttıkça oluşabilecek problemler artık ağ ile çözülebilmektedir. Sıkma-uyarma (SU) bloğunun artık ağ ile birlikte kullanımı, toplam parametre sayısını minimum düzeyde arttırırken sınıflandırma doğruluğunu arttırmakatdır. Aynı zamanda, SU bloğunun artık ağ ile birleştirilmesi geleneksel artık ağların performansını da arttırmaktadır. Önerilen yöntemin performansını test etmek için Kaggle veritabanından alınan BCCD veriseti kullanılmıştır. Uygulamalar sonucunda ortalama %99,92 doğruluk, %99,85 kesinlik, duyarlılık ve F1-skoru elde edilmiştir. Bu sonuçlar, literatürden BCCD verisetini kullanan son yıllardaki çalışmalarda yer alan ESA yöntemlerinin elde ettiği sonuçlarla karşılaştırıldı ve önerilen yöntemin daha az eğitilebilir parametre ile daha iyi sonuçlar verdiği görülmüştür.

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“…Önerilen yöntem ile elde edilen parametre sayısına en yakın 126,000 parametre ile Hosseini vd. [33] ve 133,000 parametre ile Banik vd. [34] yöntemlerinde elde edilmiştir.…”

Section: Deneysel Sonuçlar (Experimental Results)unclassified

Classification of White Blood Cells using the Squeeze-Excitation Residual Network

FIRAT

2023

Bilişim Teknolojileri Dergisi

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“…They share the phagocytic ability of neutrophils, break down bacteria, and remove waste from the blood. They have a longer life span compared with other leukocytes ( 20 ).…”

Section: Methodsmentioning

confidence: 99%

“…The grid search (GS) and random search (RS) hyperparameter optimization methods were used by Hosseini et al. ( 20 ) to categorize images of four different categories of leukocytes. ACC of 99% on the training set and of 97% on the validation set was effectively obtained by the given hybrid technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Houssein,

Mohamed,

Abdel Samee

et al. 2023

Front. Oncol.

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BackgroundThe examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification.Materials and MethodsImages of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance.ResultsThe dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.ConclusionUsing a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

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“…However, it is computationally expensive and might not be suitable in cases where the model contains a large number of hyperparameters or where the model is computationally expensive. Another universal method of hyperparameter optimization is random search, in which upper and lower limits for all hyperparameters also need to be artifcially specifed, but the method assumes that diferent hyperparameters are not equally important for the model performance, so it is more efcient to use randomly distributed points rather than uniformly distributed points to cover the parameter space [51,54]. In practice, it has been found that random search is also easy to implement and performs better than grid search, because it can fnd the optimal combination of hyperparameters almost as well as grid search and in signifcantly less time [51].…”

Section: Hyperparameter Optimizationmentioning

confidence: 99%

Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs

Liang

Liu

Kaewunruen

et al. 2023

Structural Control and Health Monitoring

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Excessive external vibrations could affect the normal functioning and integrity of sensitive buildings such as laboratories and heritage buildings. Usually, these buildings are exposed to multiple external vibration sources simultaneously, so the monitoring and respective evaluation of the vibration from various sources is necessary for the design of targeted vibration mitigation measures. To classify the sources of vibration accurately and efficiently, the advanced hybrid models of the convolutional neural network (CNN) and long short-term memory (LSTM) network were built in this study, and the models are driven by the extensive data of external vibration recorded in Beijing, and the parametric studies reveal that the proposed optimal model can achieve an accuracy of over 97% for the identification of external vibration sources. Finally, a real-world case study is presented, in which external vibration monitoring was carried out in a laboratory and the proposed CNN+LSTM model was used to identify the sources of vibration in the monitoring so that the impact of vibration from each source on the laboratory was analyzed statistically in detail. The results demonstrate the necessity of this study and its feasibility for engineering applications.

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Leukocytes Image Classification Using Optimized Convolutional Neural Networks

Cited by 16 publications

References 54 publications

Classification of White Blood Cells using the Squeeze-Excitation Residual Network

Classification of White Blood Cells using the Squeeze-Excitation Residual Network

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs

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