Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey: E3S Web of Conferences

Vivek Patel, V. Chaurasia, R. Mahadeva, Abhijeet Ghosh, S. Dixit, B. Suthar, V. Gupta, D. Siri, Y.J. Nagendra Kumar, N. Dhaliwal, H. Bommala, K. Kumar, Swadesh Kumar S. (Editor)

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms. © 2023 EDP Sciences. All rights reserved.
Original languageBritish English
StatePublished - 2023


  • Breast cancer Diagnosis
  • Convolutional Neural Network
  • Cross-level attention
  • Deep Learning
  • Histopathology Image
  • Transfer learning


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