Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

Shovon, Md Sakib Hossain; Islam, Md. Jahidul; Nabil, Mohammed Nawshar Ali Khan; Molla, Md. Mohimen; Jony, Akinul Islam; Mridha, M. F.

doi:10.3390/diagnostics12112825

Cited by 20 publications

(9 citation statements)

References 39 publications

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“…Nonetheless, in this method, it is necessary to analyze the prediction results of patch-level images during the classification process, which increases the overheads. Moreover, Sakib Hossain Shovon et al [20] put forward an improved TL architecture-HE-HER2Net, using the same BCI dataset as ours, and the accuracy rate reached 87%. Compared with HAHNet proposed in this paper, HE-HER2Net had lower accuracy.…”

Section: Introductionmentioning

confidence: 73%

HAHNet: A convolutional neural network for HER2 status classification of breast cancer

Wang

Zhu

Chen

et al. 2023

Preprint

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Background: Breast cancer is a major health problem for women. Human epidermal growth factor receptor-2 (HER2) is a very important diagnostic and prognostic factor for breast cancer, and HER2 status classification is essential for the development of treatment plans for breast cancer. Generally speaking, pathologists will adopt immunohistochemistry (IHC) to assess HER2 status, which requires additional economic costs. Furthermore, the manual assessment of HER2 status is time-consuming and error-prone. In recent years, deep learning has been widely used in medical field and has attained great achievements. However, the existing deep learning methods for HER2 status classification of conventional hematoxylin and eosin (H&E) stained images are not accurate enough. Results: To address these problems, a neural network model named HAHNet is proposed in this paper. HAHNet combines multi-scale features with attention mechanisms, which is able to directly classify HER2 status of H&E stained histological images of breast cancer. Typically, the HAHNet network mainly includes convolution preprocessing, attention mechanism, downsampling, and multi-scale feature extraction. The experimental results show that HAHNet outperforms other existing methods with regard to six metrics of Accuracy, Sensitivity, Precision, F-score, MCC, and AUC. Conclusions: Collectively, the above experiments demonstrate that our proposed HAHNet achieves high performance in classifying the HER2 status of breast cancer using only H&E stained samples, which can be used in case classification and helps to reduce the cost required for diagnosis.

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Section: Introductionmentioning

confidence: 73%

HAHNet: A convolutional neural network for HER2 status classification of breast cancer

Wang

Zhu

Chen

et al. 2023

Preprint

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show abstract

“…Nonetheless, in this method, it is necessary to analyze the prediction results of patch-level images during the classification process, which increases the overheads. Moreover, Sakib Hossain Shovon et al [ 21 ] put forward an improved TL architecture HE-HER2Net, using the same BCI dataset as ours, and the accuracy rate reached 87%, this work has achieved promising results, but the prediction accuracy still needs to be improved. Compared with HAHNet proposed in this paper, HE-HER2Net had lower accuracy.…”

Section: Introductionmentioning

confidence: 82%

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

Wang,

Zhu,

Chen

et al. 2023

BMC Bioinformatics

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Objective Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists. Results This paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods. Conclusions According to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients.

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“…An experiment shows [43] adding GAP, DO, BN, and Dense layers to the TL model improved the model performance significantly, reduced underfitting and overfitting problems, thinned the model, and trained faster. After integrating these enhancement approaches, PlantDet obtained enhanced results in all evaluation matrices.…”

Section: P (Y) =mentioning

confidence: 99%

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

et al. 2023

Self Cite

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Plant disease is a significant health concern among all living creatures. Early diagnosis can help farmers takenecessary steps to cure the disease and accelerate the production rate efficiently. Our research has beenconducted with five most common rice leaf diseases, such as bacterial leaf blight, brown spot, leaf blast,leaf scald, and narrow brown spot, including healthy class, and two categories of betel leaf, such as healthyand unhealthy class. A robust new deep ensemble model, based on InceptionResNetV2, EfficientNetV2L,and Xception, has been proposed, known as, PlantDet, in this research. PlantDet solves not only underfitting problems but also leverage nourished performances simultaneously for scarce dataset of the sparse number of different background image dataset. PlantDet integrates efficient data augmentation, preprocessing, Global Average Pooling layer, Dropout mechanism, L2 regularizers, PRelu activation function, Batch Normalization layers, and more Dense layers that make the model more robust compared to all existing models and help to handle underfitting and overfitting problems while maintaining high performance. PlantDet exceeds the previous state-of-art model for the Rice Leaf dataset with an Accuracy of 98.53%, a Precision of 98.50%, a Recall of 98.35%, a F1 of 98.42% and a Specificity of 99.71%. In addition, for the Betel Leaf dataset, PlantDet also surpassed all existing base models, including several robust ensemble models. Finally, Grad-CAM and Score-CAM have been accomplished with the Xception method to explain the model performances particularly to elaborate how the Deep Learning (DL) models works for this complex dataset. Score-CAM slightly outperformed Grad-CAM++ in terms of localizing the predicted area.

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Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

Cited by 20 publications

References 39 publications

HAHNet: A convolutional neural network for HER2 status classification of breast cancer

HAHNet: A convolutional neural network for HER2 status classification of breast cancer

HAHNet: a convolutional neural network for HER2 status classification of breast cancer

PlantDet: A Robust Multi-Model Ensemble Method Based on Deep Learning For Plant Disease Detection

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