Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

¹ Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, 462003, India
² Khalifa University of Science and Technology, Abu Dhabi, 127788, United Arab Emirates
³ Division of Research and Innovation, Uttaranchal University, Dehradun, 248012, India
⁴ Amity Institute of English Studies and Research, Amity University, Patna, Bihar, 801503, India
⁵ Peter the Great St Petersburg Polytechnic University, St Petersburg, 195251, Russia
⁶ GRIET, Bachupally, Hyderabad, Telangana, 500090, India
⁷ Lovely Professional University, Phagwara, Punjab, 144001, India
⁸ K G Reddy College of Engineering & Technology, Hyderabad, Telangana, 501504, India
⁹ K R Mangalam University, Gurgaon, Haryana, 122103, India

^* Corresponding author: vivekpatel.iet46@gmail.com

Abstract

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.