Multiclassification Model of Histopathological Breast Cancer Based on Deep Neural Network

Mohammed, Faris E.; Zghal, Nadia Smaoui; Aissa, Dalinda Ben; El-Gayar, M. M.

doi:10.1109/ssd54932.2022.9955814

2022 19th International Multi-Conference on Systems, Signals &Amp; Devices (SSD) 2022

DOI: 10.1109/ssd54932.2022.9955814

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Multiclassification Model of Histopathological Breast Cancer Based on Deep Neural Network

Faris E. Mohammed

Nadia Smaoui Zghal

Dalinda Ben Aissa

et al.

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2024

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Cited by 2 publications

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Automatic approach for breast cancer detection based on deep belief network using histopathology images

Karthiga,

Narasimhan,

N.Raju

et al. 2024

Multimed Tools Appl

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Automatic approach for breast cancer detection based on deep belief network using histopathology images

Karthiga,

Narasimhan,

N.Raju

et al. 2024

Multimed Tools Appl

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TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet

Zhang,

Pan,

Yang

et al. 2024

CBIO

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background: Breast tumor is among the most malignant tumors and early detection can improve patient’s survival rate. Currently, mammography is the most reliable method for diagnosing breast tumor because of high image resolution. Because of the rapid development of medical and artificial intelligence techniques, computer-aided diagnosis technology can greatly improve the detection accuracy of breast tumors and medical imaging has begun to use deep-learning-based approaches. In this study, the TumorDet model is proposed to detect the benign and malignant lesions of breast tumor, which has positive significance for assisting doctors in diagnosis. objective: We use the proposed TumorDet to analyze and predict breast tumors on the real MRI dataset. method: (1) We introduce an adaptive gamma correction (AGC) method to balance brightness equalization and increase the contrast of mammography images; (2) we use the ShuffleNet model to exchange information between different feature layers and extract the hidden high-level features of medical images; and (3) we use the transfer learning method to fine-tune the ShuffleNet model and obtain the optimal parameters. result: The proposed TumorDet model has shown that accuracy, sensitivity, and specificity reach 90.43%, 89.37%, and 87.81%, respectively. TumorDet performs well in the breast tumor detection task. In addition, we use the proposed TumorDet to conduct experiments on other tasks, such as forest fires, and the robustness of TumorDet is proved by experimental results. conclusion: TumorDet employs the ShuffleNet model to exchange information between different feature layers without increasing the number of network parameters and applies transfer learning methods to further extract the basic features of medical images by fine-tuning. The model is beneficial for the localization and classification of breast tumors and also performs well in forest fire detection.

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Multiclassification Model of Histopathological Breast Cancer Based on Deep Neural Network

Cited by 2 publications

References 16 publications

Automatic approach for breast cancer detection based on deep belief network using histopathology images

Automatic approach for breast cancer detection based on deep belief network using histopathology images

TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet

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