158 research outputs found
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
Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique
White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non- leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method
This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of
blood cancer prevalent in children and teenagers, characterized by the rapid
proliferation of immature white blood cells (WBCs). These atypical cells can
overwhelm healthy cells, leading to severe health consequences. Early and
accurate detection of ALL is vital for effective treatment and improving
survival rates. Traditional diagnostic methods are time-consuming, costly, and
prone to errors. The paper proposes an automated detection approach using
computer-aided diagnostic (CAD) models, leveraging deep learning techniques to
enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes
various transfer learning models like ResNet101V2, VGG19, InceptionV3, and
InceptionResNetV2 for classifying ALL. The methodology includes using the Local
Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and
reliability of the AI system's predictions. This approach is critical for
overcoming the "black box" nature of AI, where decisions made by models are
often opaque and unaccountable. The paper highlights that the proposed method
using the InceptionV3 model achieved an impressive 98.38% accuracy,
outperforming other tested models. The results, verified by the LIME algorithm,
showcase the potential of this method in accurately identifying ALL, providing
a valuable tool for medical practitioners. The research underscores the impact
of explainable artificial intelligence (XAI) in medical diagnostics, paving the
way for more transparent and trustworthy AI applications in healthcare
Novel Pipeline for Diagnosing Acute Lymphoblastic Leukemia Sensitive to Related Biomarkers
Acute Lymphoblastic Leukemia (ALL) is one of the most common types of
childhood blood cancer. The quick start of the treatment process is critical to
saving the patient's life, and for this reason, early diagnosis of this disease
is essential. Examining the blood smear images of these patients is one of the
methods used by expert doctors to diagnose this disease. Deep learning-based
methods have numerous applications in medical fields, as they have
significantly advanced in recent years. ALL diagnosis is not an exception in
this field, and several machine learning-based methods for this problem have
been proposed. In previous methods, high diagnostic accuracy was reported, but
our work showed that this alone is not sufficient, as it can lead to models
taking shortcuts and not making meaningful decisions. This issue arises due to
the small size of medical training datasets. To address this, we constrained
our model to follow a pipeline inspired by experts' work. We also demonstrated
that, since a judgement based on only one image is insufficient, redefining the
problem as a multiple-instance learning problem is necessary for achieving a
practical result. Our model is the first to provide a solution to this problem
in a multiple-instance learning setup. We introduced a novel pipeline for
diagnosing ALL that approximates the process used by hematologists, is
sensitive to disease biomarkers, and achieves an accuracy of 96.15%, an
F1-score of 94.24%, a sensitivity of 97.56%, and a specificity of 90.91% on ALL
IDB 1. Our method was further evaluated on an out-of-distribution dataset,
which posed a challenging test and had acceptable performance. Notably, our
model was trained on a relatively small dataset, highlighting the potential for
our approach to be applied to other medical datasets with limited data
availability
Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks
Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow. © 2013 IEEE
Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia
Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1
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