White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches ...White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.展开更多
White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red b...White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.展开更多
AIM: To examine characteristics of patients with blood urea nitrogen(BUN) levels higher and lower than the normal limit.METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively. During ...AIM: To examine characteristics of patients with blood urea nitrogen(BUN) levels higher and lower than the normal limit.METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively. During this time, 3296 patients underwent upper endoscopy. In total, 50 male(69.2 ± 13.2 years) and 26 female(72.3 ± 10.2 years) patients were assessed. Patients were divided into two groups based on BUN levels: higher than the normal limit(21.0 mg/d L)(H) and lower thanthe normal limit(L). One-way analysis of variance was performed to reveal differences in the variables between the H and L groups. Fisher's exact test was used to compare the percentage of patients with gastric ulcer or gastric cancer in the H and L groups.RESULTS: White blood cell count was higher in the H group than in the L group(P = 0.0047). Hemoglobin level was lower in the H group than in the L group(P = 0.0307). Glycated hemoglobin was higher in the H group than in the L group(P = 0.0264). The percentage of patients with gastric ulcer was higher in the H group(P = 0.0002). The H group contained no patients with gastric cancer.CONCLUSION: Patients with BUN ≥ 21 mg/d L might have more severe upper gastrointestinal bleeding.展开更多
Cell population data(CPD)is regarded as the fingerprint of a blood cell at a given moment.CPD parameters harbor information associated with cell morphology and can be automatically generated using modern hematological...Cell population data(CPD)is regarded as the fingerprint of a blood cell at a given moment.CPD parameters harbor information associated with cell morphology and can be automatically generated using modern hematological analyzers.Various studies have revealed many unique clinical applications for CPD,especially for infectious diseases,such as sepsis.For example,one monocyte-related CPD parameter is the monocyte distribution width(MDW),which can be generated using a Beckman Coulter hematological analyzer.MDW has received FDA and CE approval for aiding in sepsis diagnosis in adult patients in the emergency department.Additionally,MDW can serve as a diagnostic biomarker in patients infected with SARS-CoV-2.CPD has also been widely explored for possible clinical applications beyond infectious dis-eases,such as for predicting myelodysplastic syndromes,screening for he-matological malignancies,and detecting sterile inflammation.CPD parameter measurements are easily obtained and quite cost-effective,making them practical for clinical use.However,there are some potential drawbacks of CPD parameters.Some pre-analytical conditions can affect CPD values.Further-more,CPD are specific to certain hematological analyzers and the result cannot be transferred between different analyzers.The practical usefulness of CPD reference intervals is also still questionable.In this review,wesummarize the current studies related to CPD and its clinical applications.Additional well-designed clinical studies related to CPD are still expected.展开更多
基金The Deanship of Scientific Research (DSR)at King Abdulaziz University (KAU),Jeddah,Saudi Arabia has funded this project,under Grant No.KEP-1–120–42.
文摘White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.
基金This research project was supported by the Deanship of Scientific Research,Prince Sattam Bin Abdulaziz University,KSA,Project Grant No.2021/01/18613.
文摘White blood cells(WBC)are immune system cells,which is why they are also known as immune cells.They protect the human body from a variety of dangerous diseases and outside invaders.The majority of WBCs come from red bone marrow,although some come from other important organs in the body.Because manual diagnosis of blood disorders is difficult,it is necessary to design a computerized technique.Researchers have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model selection.We proposed an automated deep learning approach for classifying white blood disorders in this paper.The data augmentation approach is initially used to increase the size of a dataset.Then,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen dataset.On the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling layer.The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique.Following that,machine learning classifiers are used to classify the selected best features for final classification.The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.
文摘AIM: To examine characteristics of patients with blood urea nitrogen(BUN) levels higher and lower than the normal limit.METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively. During this time, 3296 patients underwent upper endoscopy. In total, 50 male(69.2 ± 13.2 years) and 26 female(72.3 ± 10.2 years) patients were assessed. Patients were divided into two groups based on BUN levels: higher than the normal limit(21.0 mg/d L)(H) and lower thanthe normal limit(L). One-way analysis of variance was performed to reveal differences in the variables between the H and L groups. Fisher's exact test was used to compare the percentage of patients with gastric ulcer or gastric cancer in the H and L groups.RESULTS: White blood cell count was higher in the H group than in the L group(P = 0.0047). Hemoglobin level was lower in the H group than in the L group(P = 0.0307). Glycated hemoglobin was higher in the H group than in the L group(P = 0.0264). The percentage of patients with gastric ulcer was higher in the H group(P = 0.0002). The H group contained no patients with gastric cancer.CONCLUSION: Patients with BUN ≥ 21 mg/d L might have more severe upper gastrointestinal bleeding.
基金the basic and applied basic research projects of Guangdong Province,Grant/Award Number:2021A1515220040。
文摘Cell population data(CPD)is regarded as the fingerprint of a blood cell at a given moment.CPD parameters harbor information associated with cell morphology and can be automatically generated using modern hematological analyzers.Various studies have revealed many unique clinical applications for CPD,especially for infectious diseases,such as sepsis.For example,one monocyte-related CPD parameter is the monocyte distribution width(MDW),which can be generated using a Beckman Coulter hematological analyzer.MDW has received FDA and CE approval for aiding in sepsis diagnosis in adult patients in the emergency department.Additionally,MDW can serve as a diagnostic biomarker in patients infected with SARS-CoV-2.CPD has also been widely explored for possible clinical applications beyond infectious dis-eases,such as for predicting myelodysplastic syndromes,screening for he-matological malignancies,and detecting sterile inflammation.CPD parameter measurements are easily obtained and quite cost-effective,making them practical for clinical use.However,there are some potential drawbacks of CPD parameters.Some pre-analytical conditions can affect CPD values.Further-more,CPD are specific to certain hematological analyzers and the result cannot be transferred between different analyzers.The practical usefulness of CPD reference intervals is also still questionable.In this review,wesummarize the current studies related to CPD and its clinical applications.Additional well-designed clinical studies related to CPD are still expected.
