8,695 research outputs found
Identification of Sickle Cell Anemia Using Deep Neural Networks
A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PD
A Review on Classification of White Blood Cells Using Machine Learning Models
The machine learning (ML) and deep learning (DL) models contribute to
exceptional medical image analysis improvement. The models enhance the
prediction and improve the accuracy by prediction and classification. It helps
the hematologist to diagnose the blood cancer and brain tumor based on
calculations and facts. This review focuses on an in-depth analysis of modern
techniques applied in the domain of medical image analysis of white blood cell
classification. For this review, the methodologies are discussed that have used
blood smear images, magnetic resonance imaging (MRI), X-rays, and similar
medical imaging domains. The main impact of this review is to present a
detailed analysis of machine learning techniques applied for the classification
of white blood cells (WBCs). This analysis provides valuable insight, such as
the most widely used techniques and best-performing white blood cell
classification methods. It was found that in recent decades researchers have
been using ML and DL for white blood cell classification, but there are still
some challenges. 1) Availability of the dataset is the main challenge, and it
could be resolved using data augmentation techniques. 2) Medical training of
researchers is recommended to help them understand the structure of white blood
cells and select appropriate classification models. 3) Advanced DL networks
such as Generative Adversarial Networks, R-CNN, Fast R-CNN, and faster R-CNN
can also be used in future techniques.Comment: 23 page
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells
Leukemia (blood cancer) is an unusual spread of White Blood Cells or
Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose
leukemia by looking at a person's blood sample under a microscope. They
identify and categorize leukemia by counting various blood cells and
morphological features. This technique is time-consuming for the prediction of
leukemia. The pathologist's professional skills and experiences may be
affecting this procedure, too. In computer vision, traditional machine learning
and deep learning techniques are practical roadmaps that increase the accuracy
and speed in diagnosing and classifying medical images such as microscopic
blood cells. This paper provides a comprehensive analysis of the detection and
classification of acute leukemia and WBCs in the microscopic blood cells.
First, we have divided the previous works into six categories based on the
output of the models. Then, we describe various steps of detection and
classification of acute leukemia and WBCs, including Data Augmentation,
Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction),
Classification, and focus on classification step in the methods. Finally, we
divide automated detection and classification of acute leukemia and WBCs into
three categories, including traditional, Deep Neural Network (DNN), and mixture
(traditional and DNN) methods based on the type of classifier in the
classification step and analyze them. The results of this study show that in
the diagnosis and classification of acute leukemia and WBCs, the Support Vector
Machine (SVM) classifier in traditional machine learning models and
Convolutional Neural Network (CNN) classifier in deep learning models have
widely employed. The performance metrics of the models that use these
classifiers compared to the others model are higher
A review on a deep learning perspective in brain cancer classification
AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm
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