ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification

Jawahar, Malathy; Sharen, H; L, Jani Anbarasi; Gandomi, Amir H.

doi:10.1016/j.compbiomed.2022.105894

Cited by 24 publications

(8 citation statements)

References 12 publications

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“…Among deep-learning strategies, VGG16 has been commonly reported for leukemia detection [36][37][38], with outstanding results in one of the herein tested databases (accuracy of 97.04 ± 1.21 for D1) [39,40]. The present work extends this evaluation to the D2 database for the VGG16, while the D1 test is performed under a five-fold scheme which is considered more general than a simple leave-on-out strategy, as already reported [40].…”

Section: Experiments 3: Evaluating State-of-the-art Strategies In Bla...supporting

confidence: 60%

“…This experiment takes two of the best state‐of‐the‐art CNNs for this task, and separately trains each to classify blast and nonblasts in D1 and D2, that is, VGG16 [28] and RESNEXT [29] are considered as baseline. Among deep‐learning strategies, VGG16 has been commonly reported for leukemia detection [36–38], with outstanding results in one of the herein tested databases (accuracy of 97.04 ± 1.21 for D1) [39, 40]. The present work extends this evaluation to the D2 database for the VGG16, while the D1 test is performed under a five‐fold scheme which is considered more general than a simple leave‐on‐out strategy, as already reported [40].…”

Section: Methodssupporting

confidence: 52%

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Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

et al. 2023

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Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for White Blood Cell (WBC) Counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter‐ and intraobserver variability. The present work introduces an image combined‐descriptor to detect blasts and determine their probable lineage. This strategy uses an Intra‐nucleus Mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner‐nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically‐segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co‐occurrence matrix representation. Both InMoP and Haralick‐based descriptors are calculated using the b‐channel from Lab color‐space. The combined‐descriptor is assessed by differentiating blasts from non‐leukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database‐D1 (n=260) is composed of healthy and Acute Lymphoid Leukemia (ALL) single cell images, and second database‐D2 contains Acute Myeloid Leukemia (AML) blasts (n=3,294) and non‐blast (n=15,071) cell images. In a first experiment, blasts vs non‐blast differentiation is performed by training with a subset of D2 (n=6,588) and testing in D1 (n=260), obtaining a training AUC of 0.991±0.002 and AUC=0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state‐of‐the‐art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from non‐blast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1‐score.This article is protected by copyright. All rights reserved.

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Section: Experiments 3: Evaluating State-of-the-art Strategies In Bla...supporting

confidence: 60%

Section: Methodssupporting

confidence: 52%

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

et al. 2023

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“…DL frameworks have been devised to discriminate between molecular subtypes in various cancers [ 144 ]. In the context of leukemia, different DL framework (AMLnet [ 145 ], CMLcGAN [ 146 ], ALNett [ 147 ]) has been applied to the diagnosis and classification of leukemia from medical images. The rapid emergence of AI that integrate and analyze omics data is happening in parallel with advancements in single-cell technologies.…”

Section: Perspectivesmentioning

confidence: 99%

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Liu,

Jiang,

et al. 2024

Exp Hematol Oncol

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Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia’s clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

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“…The results of cluster and discriminant analyses for various types of pediatric acute leukemia revealed that a combination of DL analysis and microscopic blood images facilitated the classification of acute leukemia and outperformed expert hematologists with an accuracy of more than 98% [20,21]. The utility of AI in the automatic analysis of microscopy images represented diagnostic accuracy of around 95% in acute promyelocytic leukemia [22], acute lymphoblastic leukemia (ALL) [23,24], and leukemic B-lymphoblast [25], which was optimized by a hybrid model using a genetic algorithm and a residual CNN reaching an accuracy of 98.46% [26]. The DL analysis was also applied in the classification of ALL, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML) using bone marrow cell microscopy images [27].…”

Section: Non-solid Tumor Diagnosismentioning

confidence: 99%

Artificial intelligence applications in pediatric oncology diagnosis

Zhang

2023

Exploration of Targeted Anti-Tumor Therapy

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Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.

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ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification

Cited by 24 publications

References 12 publications

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

Intra‐nucleus mosaic pattern (InMop) and whole‐cell Haralick combined‐descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Artificial intelligence applications in pediatric oncology diagnosis

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