Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Sriram, G; Babu, T. R. Ganesh; Praveena, R.; Anand, Jose

doi:10.32604/mcb.2022.016966

Cited by 21 publications

(9 citation statements)

References 19 publications

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“…VGG16 [ 26 ] is a pre-trained model consisting of sixteen convolutional layers followed by three dense layers. The convolutional layers are composed of 3 × 3 filters and the max-pooling layer with a 2 × 2 window is applied after every two convolutional layers.…”

Section: Proposed Deep Feature Selection Based Approachmentioning

confidence: 99%

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

et al. 2023

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Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.

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Section: Proposed Deep Feature Selection Based Approachmentioning

confidence: 99%

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

et al. 2023

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“…In all work they concentrate only on spatial features. In this work [10] has presented segmentation algorithm of Hue Saturation Value color based on watershed and VGG16 (Visual Geometric Group) architecture has been used for classification and counting WBC type from segmented images. This work [11] proposed a automatic detection and classification of AML in blood smear and for segmentation, K-means algorithm is used and combined of spatial and spectral features are used for classification.…”

Section: Related Workmentioning

confidence: 99%

Classification of WBC cell classification using fully connected convolution neural network

Kannan¹,

Babu²,

Praveena³

et al. 2023

J. Phys.: Conf. Ser.

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White blood cells (WBCs) are cells that is key factor of the immune systems which is help to our body fight off contagions and other diseases. In order to enhance the diagnosis of various diseases in medical field by using image processing techniques from the blood cells. In that, Leukemia is associated with one type of cancer of the blood and bone marrow. It is look like spongy tissue inside the bones where blood cells are made. In this paper, a fully connected. Convolution neural network is used to segmented and classification of blood cell microscope WBC images for healthy and unhealthy conditions. The performance of the classifier was analyzed. The accuracy sensitivity specificity and pression are 96.84%, 96.26%,97.35% and 96.39% respectively.

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“…Four datasets were utilized to attain 97.82% accuracy, which is better than the obtained outcomes in [4]. G. Sriram et al,in [6], implemented a model to categorize leukemia using the VGG-16 CNN model when applied to a single dataset of nearly 700 images. This model classified only two types, which were ALL and AML, and attained nearly 98% accuracy, which is less than what the proposed algorithm obtained on four datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Acute leukemia and chronic leukemia are considered the most common types of blood cancer that occur often and are widely diagnosed around the world [3][4][5]. Uncontrolled changes in white blood cells stimulate the birth of too many cells or generate unneeded behaviors [6]. Recently, physicians named four types of leukemia: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML) [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Uncontrolled changes in white blood cells stimulate the birth of too many cells or generate unneeded behaviors [6]. Recently, physicians named four types of leukemia: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML) [6][7][8][9][10][11]. These types were categorized according to the intensity levels of tumor cells [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis

Alzahrani,

Alsheikhy,

Shawly

et al. 2023

Algorithms

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Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient’s life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services.

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Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Cited by 21 publications

References 19 publications

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

Classification of WBC cell classification using fully connected convolution neural network

A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis

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