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

Atteia, Ghada

doi:10.32604/csse.2023.029597

Cited by 10 publications

(12 citation statements)

References 40 publications

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“…By automating the understanding of medical images, researchers can reduce the chances of errors and ensure precise and timely diagnosis. This technology can revolutionize the medical domain by equipping doctors and patients with more efficient and persuasive diagnostic mechanisms and, eventually, more promising results [ 2 , 6 ]. Early detection of leukemia is critical: therefore, an automated diagnosis system can reduce the mortality rate in the medical field at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification

2023

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Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art.

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Section: Introductionmentioning

confidence: 99%

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification

2023

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“…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%

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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“…Machine and deep learning techniques have proven high prediction performance in several life fields. For instance, machine learning has been used for the medical diagnosis of many diseases [20][21][22][23], cyberbullying detection [24], environmental monitoring [25], augmentation of turbulence models [26], management of vegetated water resources [27,28], and in other applications. In oceanography and Earth sciences, ML has a diverse range of real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data

2022

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Predicting sea wave parameters such as significant wave height (SWH) has recently been identified as a critical requirement for maritime security and economy. Earth observation satellite missions have resulted in a massive rise in marine data volume and dimensionality. Deep learning technologies have proven their capabilities to process large amounts of data, draw useful insights, and assist in environmental decision making. In this study, a new deep-learning-based hybrid feature selection approach is proposed for SWH prediction using satellite Synthetic Aperture Radar (SAR) mode altimeter data. The introduced approach integrates the power of autoencoder deep neural networks in mapping input features into representative latent-space features with the feature selection power of the principal component analysis (PCA) algorithm to create significant features from altimeter observations. Several hybrid feature sets were generated using the proposed approach and utilized for modeling SWH using Gaussian Process Regression (GPR) and Neural Network Regression (NNR). SAR mode altimeter data from the Sentinel-3A mission calibrated by in situ buoy data was used for training and evaluating the SWH models. The significance of the autoencoder-based feature sets in improving the prediction performance of SWH models is investigated against original, traditionally selected, and hybrid features. The autoencoder–PCA hybrid feature set generated by the proposed approach recorded the lowest average RMSE values of 0.11069 for GPR models, which outperforms the state-of-the-art results. The findings of this study reveal the superiority of the autoencoder deep learning network in generating latent features that aid in improving the prediction performance of SWH models over traditional feature extraction methods.

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

Cited by 10 publications

References 40 publications

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification

Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification

A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis

Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data

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