Acute Myeloid Leukemia (AML) Detection Using AlexNet Model

Shaheen, Maneela; Khan, Rafiullah; Biswal, Rajesh Roshan; Ullah, Mohib; Khan, Atif; Uddin, M. Irfan; Zareei, Mahdi; Waheed, Abdul

doi:10.1155/2021/6658192

Cited by 30 publications

(9 citation statements)

References 33 publications

(35 reference statements)

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“…The World Health Organization (WHO) reported that around 19 million patients were diagnosed with cancer in 2020 [ 1 – 3 ]. Among them, 10 million patients died.…”

Section: Introductionmentioning

confidence: 99%

“…Blood is responsible for delivering oxygen and nutrients to the cells inside the human body. Furthermore, it transports the produced waste from them [ 3 , 4 ]. Two types of leukemia have been detected and identified: acute and chronic [ 2 , 5 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…ALL occurs in children of age between 3 and 7 [ 2 ], and nearly two-thirds of diagnosed reported cases have occurred before age of 6 [ 2 ]. WHO claims that leukemia is the primary source of high death rates from cancer [ 3 ]. Chronic leukemia grows slower than acute leukemia.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 3 ], Shaheen et al developed a model to classify and detect AML in microscopic images based on the AlexNet approach. The authors claimed that their model reached 89% accuracy and nearly 88% precision on a dataset that contained 4 thousand blood smears.…”

Section: Introductionmentioning

confidence: 99%

“…A comparison study between AlexNet and LeNet was conducted, and it showed that there was a slight difference between both models in which AlexNet performed better than LeNet. More information can be found in [ 3 ]. Herein, the proposed approach reaches almost 98% of accuracy for AAL and AML, while the method in [ 3 ] could only identify AML.…”

Section: Introductionmentioning

confidence: 99%

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Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

Shawly

Alsheikhy

2022

Computational Intelligence and Neuroscience

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Leukemia cancer is the most common type of cancer that occurs in childhood. The most common types are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) which affect children and adults, respectively. Several health issues occur due to these cancers. Leukemia affects the bone marrow or the lymph nodes. Leukemia produces abnormal white blood cells via the bone marrow system. The affected white blood cells are unable to perform their tasks properly. Detecting leukemia usually requires taking a blood smear from a patient and working with expert hematologists who analyze the smear with a microscope. In this paper, a method to detect ALL and AML using a deep learner classifier is developed and proposed. The method detects both types, determines their severity, and creates a message that recommends next steps to patients. This approach works based on image segmentation and a convolutional neural network (CNN) tool called AlexNet. The obtained results from the proposed approach and using MATLAB reached more than 98% accuracy. The margin exists because several operations are needed to fully detect the blood cancer. A dataset of leukemia from the Kaggle site is used to test the developed method and illustrate its effectiveness. This dataset is C-NMC_Leukemia, and it consists of nearly 10 GB worth of 15,000 images. A confusion matrix of testing images is provided to prove the correctness of the presented approach. Furthermore, a comparative analysis between the proposed algorithm and some works from the literature is presented. This analysis compares the method used to extract features, the classifier that is utilized, the accuracy, the precision, and the recall. The obtained results indicate that the proposed method outperforms other works and produces better results.

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“…The World Health Organization (WHO) reported that around 19 million patients were diagnosed with cancer in 2020 [ 1 – 3 ]. Among them, 10 million patients died.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

Shawly

Alsheikhy

2022

Computational Intelligence and Neuroscience

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Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches

Özcan,

Uyar,

Karayeğen

2024

Cytometry Pt A

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Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train‐independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train‐dependent dataset and 92.82% for train‐independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train‐independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.

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