An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model

Das, Pradeep; Sahoo, Biswajeet; Meher, Sukadev

doi:10.1109/tcbb.2022.3218590

Cited by 25 publications

(7 citation statements)

References 41 publications

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“…A schematic diagram of the integrated Hematoxylin and eosin (H&E), IHC AI model with ImmunoGenius is shown in Figure 3 . With the growth of the AI field, numerous AI tools are being developed to predict lymph node metastasis, diagnose hematological disorders, and detect breast cancer and be applicable in many other areas of science [ 25 , 26 , 27 , 28 , 29 , 30 ]. Previous research studies have shown that AI models demonstrate good performance in various aspects of tumor detection, classification, gland segmentation, and grading in many types of cancers [ 31 , 32 , 33 , 34 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

et al. 2023

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(1) Background: Differential diagnosis using immunohistochemistry (IHC) panels is a crucial step in the pathological diagnosis of hematolymphoid neoplasms. In this study, we evaluated the prediction accuracy of the ImmunoGenius software using nationwide data to validate its clinical utility. (2) Methods: We collected pathologically confirmed lymphoid neoplasms and their corresponding IHC results from 25 major university hospitals in Korea between 2015 and 2016. We tested ImmunoGenius using these real IHC panel data and compared the precision hit rate with previously reported diagnoses. (3) Results: We enrolled 3052 cases of lymphoid neoplasms with an average of 8.3 IHC results. The precision hit rate was 84.5% for these cases, whereas it was 95.0% for 984 in-house cases. (4) Discussion: ImmunoGenius showed excellent results in most B-cell lymphomas and generally showed equivalent performance in T-cell lymphomas. The primary reasons for inaccurate precision were atypical IHC profiles of certain cases, lack of disease-specific markers, and overlapping IHC profiles of similar diseases. We verified that the machine-learning algorithm could be applied for diagnosis precision with a generally acceptable hit rate in a nationwide dataset. Clinical and histological features should also be taken into account for the proper use of this system in the decision-making process.

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Section: Discussionmentioning

confidence: 99%

Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

et al. 2023

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“…Similarly, as shown in Table 7, we also compared the results of our proposed two-staged feature fusion-based stacked ensemble approach to the results of existing state-of-the-art methods for the detection of acute lymphocytic leukemia and found that it performed better on the ALLIDB2 dataset as well. In terms of Accuracy, our proposed approach outperformed the methods used in [29], [47], [10], [7], [24], [26], [25], [11] by margins of 3.7%, 0.7%, 2.7%, 1.2%, 2.2%, 2.6%, 1.2%, and 1.2%, respectively. When compared to the Precision, the methods used in [10], [7], [24], [26], [25], [11] were outperformed by 2.4%, 0.2%, 0.9%, 3.2%, 1.8%, and 0.9%, respectively.…”

Section: ) Comparative Analysismentioning

confidence: 91%

“…In terms of Accuracy, our proposed approach outperformed the methods used in [29], [47], [10], [7], [24], [26], [25], [11] by margins of 3.7%, 0.7%, 2.7%, 1.2%, 2.2%, 2.6%, 1.2%, and 1.2%, respectively. When compared to the Precision, the methods used in [10], [7], [24], [26], [25], [11] were outperformed by 2.4%, 0.2%, 0.9%, 3.2%, 1.8%, and 0.9%, respectively. When compared to the Recall, the methods used in [47], [10], [7], [24], [26], [25], [11] were outperformed by 1.4%, 2.9%, 1.3%, 3.5%, 1.9%, 0.4%, and 1.4%, respectively.…”

Section: ) Comparative Analysismentioning

confidence: 91%

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Feature Fusion Based Ensemble of Deep Networks for Acute Leukemia Diagnosis Using Microscopic Smear Images

Himel,

Hasan,

Suzuki

et al. 2024

IEEE Access

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A type of blood malignancies known as leukemia leads to elevated quantities of abnormally formed blood cells and often originates in the bone marrow. An abrupt rise in the quantity of immature blood cells is one of the distinctive characteristics of acute leukemia, that predominantly affects both children and adults. The possibility exists to drastically lower the death rate associated with acute leukemia through early detection and diagnosis. A significant and time-consuming task for the early detection of acute leukemia is microscopic examination of blood cells. In this paper, a two-staged deep learning-based computer-aided diagnosis system regarding microscopic blood smear images is proposed to assist hematologists and improve the diagnosis accuracy of acute leukemia in which for the feature fusion-based ensemble, two deep neural network branches adopting pretrained, fine-tuned EfficientNetB7 and MobileNetV3Large architectures were employed, and feature maps generated from those branches were fused and fed to the second stage of the architecture to achieve the final result. Additional dropout layers and ReLu activation were employed in the architecture to speed up the network, and compound scaling, bottleneck, and fusion architectures enhanced the overall performance. The ALLIDB1, ALLIDB2, and ASH databases were incorporated to evaluate the performances of the proposed method. The experimental findings demonstrated that the proposed approach detected acute leukemia (ALL, AML, Healthy) with an accuracy of 99.3%, F1-score of 99.3%, and AUC score of 0.997. Whereas in detecting acute lymphocytic leukemia (ALL, Healthy) and acute myeloid leukemia (AML, Healthy), the accuracies reached 100.0% and 99.8%, respectively. Thus, we believe that clinics can adopt our proposed architecture for quick and automated diagnosis.

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“…About 55% of studies used publicly available datasets, while the remaining used the most exclusive datasets collected from clinical research institutes [14]. Acute Leukemia classification with ResNet18 as backbone network using transfer learning and orthogonal SoftMax layer-based model is performed [72], reported good results.…”

Section: Introductionmentioning

confidence: 99%

GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

et al. 2023

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Across the globe, women suffer from breast cancer fatal disease. It is arising surprisingly due to a lack of awareness among them and the inconvenient reach of diagnostic systems. Many computeraided diagnostic systems have been developed for the detection of cancer. These systems have quite lower performance, so more accurate diagnosis is the need of the time to save the life of human beings. For large and imbalanced image datasets, efficient learning of the network is very important to detect and classify breast cancer more accurately. In this paper, a graph based adaptive regularized learning of deep network (GARL-Net) is proposed for more accurate breast cancer classification. Transfer learning is used for training the backbone network DenseNet121. furthermore, fine tuning of backbone network is followed by the estimated improved loss function. The improved loss function is actually graph based adaptively regularized complement cross entropy loss. The SoftMax cross entropy in itself is not sufficient to classify image samples accurately, so complement entropy technique is incorporated with the cross-entropy loss to overcome the misclassification issue. Further, by adaptive scaling of regularization term with spatial graph Laplacian basis used to adaptively penalize the complement cross entropy loss for improving the learning of the network. The performance of the proposed method is evaluated using BreakHis and BACH 2018 histopathology image datasets and outperforms the existing state-of-the-art methods and achieved 99.00% of precision, 99.40% of recall, 99.20 % of F-1score, and 99.49% of accuracy for binary classification of breast cancer image samples of BreakHis dataset. INDEX TERMSDeep learning, breast cancer image classification, complement cross entropy, transfer learning, graph Laplacian basis regularization, computer aided diagnostic.

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An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model

Cited by 25 publications

References 41 publications

Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

Feature Fusion Based Ensemble of Deep Networks for Acute Leukemia Diagnosis Using Microscopic Smear Images

GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification

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