High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets

Sahu, Adyasha; Das, Pradeep; Meher, Sukadev

doi:10.1016/j.bspc.2022.104292

Cited by 63 publications

(17 citation statements)

References 29 publications

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“… Ref. Year SSL Methods

↑ [ 39 ] 2021 × Multi-CNN Hybrid Structure 95.6% [ 40 ] 2021 × ResNet 88.9% [ 41 ] 2021 × ResNet + Binary Grey Wolf Optimization + Support Vector Machine 84.9% [ 42 ] 2022 × YOLO 95.3% [ 43 ] 2022 × CNN + Genetic Algorithm ∗∗ 92.8% [ 44 ] 2023 × ShuffleNet-ResNet ∗ 95.1% [ 45 ] 2023 × Interpretable Multitask Information Bottleneck Network ∗ 93.0% [ 46 ] 2023 × Consistent Ordinal Representations 82.2% [ 47 ] 2023 × Multi-Task Learning + Attention ∗ 91.0% [ 48 ] 2021 …”

Section: Results and Analysismentioning

confidence: 99%

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Liu,

Lv,

Lee

et al. 2023

Heliyon

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“… Ref. Year SSL Methods

Section: Results and Analysismentioning

confidence: 99%

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

Liu,

Lv,

Lee

et al. 2023

Heliyon

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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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“…Recenlty, a hybrid CNN frameworks of ShuffleNet and ResNet using mammogram image dataset has been proposed. Its an efficient model and reported accuracy of 99.17% [70].…”

Section: Hybrid Learning Approachmentioning

confidence: 97%

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.

show abstract

High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets

Cited by 63 publications

References 29 publications

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

GSDA: Generative adversarial network-based semi-supervised data augmentation for ultrasound image classification

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

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

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