Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

Muthumayil, K.; Manikandan, S.; Srinivasan, S.; Escorcia‐Gutierrez, José; Gamarra, Margarita; Mansour, Romany F.

doi:10.32604/cmc.2021.017116

Cited by 17 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the system recorded 84.3%, 97.3%, and 95.6%, for the sensitivity, specificity, and accuracy, respectively, for the subtype classification. In a neural network-based system developed by Muthumayil et al [ 41 ] to diagnose chronic lymphocytic leukemia in WBC images, the Enhanced Color Co-Occurrence Matrix algorithm was used to extract the features from the blood images and an Enhanced Virtual Neural Network was developed for the classification of the input images. The values recorded for that study are 97.8% for sensitivity, 89.9% for specificity, and 76.6% for accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Atteia

Alhussan

2022

Sensors

View full text Add to dashboard Cite

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-prone process. Deep learning-based human-centric biomedical diagnosis has recently emerged as a powerful tool for assisting physicians in making medical decisions. Therefore, numerous computer-aided diagnostic systems have been developed to autonomously identify ALL in blood images. In this study, a new Bayesian-based optimized convolutional neural network (CNN) is introduced for the detection of ALL in microscopic smear images. To promote classification performance, the architecture of the proposed CNN and its hyperparameters are customized to input data through the Bayesian optimization approach. The Bayesian optimization technique adopts an informed iterative procedure to search the hyperparameter space for the optimal set of network hyperparameters that minimizes an objective error function. The proposed CNN is trained and validated using a hybrid dataset which is formed by integrating two public ALL datasets. Data augmentation has been adopted to further supplement the hybrid image set to boost classification performance. The Bayesian search-derived optimal CNN model recorded an improved performance of image-based ALL classification on test set. The findings of this study reveal the superiority of the proposed Bayesian-optimized CNN over other optimized deep learning ALL classification models.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Atteia

Alhussan

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The System, Man and Cybernetics-Image Data Base, Iran University of Medical Science-Image Data Base, and ALL-IDB public datasets for leukemia identification were utilized to evaluate the proposed approach. In Muthumayil et al [14], the authors addressed a computer-based application technique based on Enhanced Virtual Neural Network classification for identifying and classifying CLL utilizing microscopic images of WBCs. The proposed technique attained the optimum accuracy in terms of detecting and classifying leukemia using WBCs images.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Wady¹

2022

UHD J SCI TECH

View full text Add to dashboard Cite

Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.

show abstract

“…Afterward, image features are extracted manually from the images and fed to a traditional classifier. Examples of conventional classifiers include K-Nearest Neighbors (KNN) [7], Naive Bayesian (NB) [7], SVM [7][8][9][10], and neural networks [10][11][12]. The classification performance of the classical ML methods is usually acceptable.…”

Section: Introductionmentioning

confidence: 99%

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Atteia¹

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks (CNNs) to identify the existence of ALL in blood smears. An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images. A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set. The latent features are used to perform image classification using Support Vector Machine (SVM) classifier. The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features. Moreover, the classification performance of the system with various sizes of the latent feature set is evaluated. The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

show abstract

Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

Cited by 17 publications

References 19 publications

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

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

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

Contact Info

Product

Resources

About