Hematology

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effective treatment of ALL is strongly associated with the early diagnosis of the disease. Current practice for initial ALL diagnosis is performed through manual evaluation of stained blood smear microscopy images, which is a time-consuming and error-pro...

ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions ...

IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, how...

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

OBJECTIVE: Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hy...

An analysis of the influence of transfer learning when measuring the tortuosity of blood vessels.

BACKGROUND AND OBJECTIVE: Convolutional Neural Networks (CNNs) can provide excellent results regarding the segmentation of blood vessels. One importan...

A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms.

Brain tumors are one of the most dangerous diseases that affect human health and maybe result in death. Detection of brain tumors can be made by using...

Detection of WBC, RBC, and Platelets in Blood Samples Using Deep Learning.

A blood count is one of the most important diagnostic tools in medicine and one of the most common procedures. It can reveal important changes in the ...

The 'prostate-muscle index': a simple pelvic cavity measurement predicting estimated blood loss and console time in robot-assisted radical prostatectomy.

This study was to show the impact of 'prostate-muscle index (PMI)', which we developed as a novel pelvic cavity measurement, in patients undergoing ro...

Provisional Decision-Making for Perioperative Blood Pressure Management: A Narrative Review.

Blood pressure (BP) is a basic determinant for organ blood flow supply. Insufficient blood supply will cause tissue hypoxia, provoke cellular oxidativ...

Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network.

BACKGROUND AND OBJECTIVE: Leukemia represents 30% of all pediatric cancers and is considered the most common malignancy affecting adults and children....

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net.

To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method bas...

Nitrogen and Chemical Control Management Improve Yield and Quality in High-Density Planting of Maize by Promoting Root-Bleeding Sap and Nutrient Absorption.

High-density planting aggravates competition among plants and has a negative impact on plant growth and productivity. Nitrogen application and chemica...

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and k...

See-Through Vision With Unsupervised Scene Occlusion Reconstruction.

Among the greatest of the challenges of minimally invasive surgery (MIS) is the inadequate visualisation of the surgical field through keyhole incisio...

Heterogeneity in Blood Biomarker Trajectories After Mild TBI Revealed by Unsupervised Learning.

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed an...

The robotic-assisted extended "Sistrunk" approach for tumors of the upper aerodigestive tract with limited transoral access: First description of oncological and functional outcomes.

We report on the first clinical experience with the robotic-assisted extended "Sistrunk" approach (RESA) for access to constrained spaces of the upper...

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients.

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a p...

Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of model...

Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.

Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading c...

Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models.

Three artificial intelligence (AI) data-driven techniques, including artificial neural network (ANN), support vector regression (SVR), and adaptive ne...