Classification of breast cancer types, sub-types and grade from histopathological images using deep learning technique

Breast cancer is one of the most serious diseases that affect women, so it must be discovered in the early stages to avoid complications such as redness of the skin, pain in the armpits or breast, and discharge from a nipple, possibly containing blood. Recently, the CAD system that is based on the classification of microscopic image play a vital rule to limit cancer disease and reduce cases. Microscopic image is the currently recommended image system used to detect cancer. A computer-aided diagnosis system will help radiologists to accurately detection of cancerous cells and achieve the best result. This paper proposes a deep learning technique that exploits CAD system features and microscopic images to fight breast cancer. The proposed technique builds a classification model based on the DenseNet-161 deep learning method. The proposed model classifies the microscopic images of breast cancer into benign with four types and malignant with four types. Our proposed technique is experimentally tested and the result confirmed that a proposed technique outperforms baseline techniques.

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A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

Recent Patents on Engineering ◽

10.2174/1872212114999201109205421 ◽

2020 ◽

Vol 14 ◽

Author(s):

Lahari Tipirneni ◽

Rizwan Patan

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Convolutional Neural Network ◽

Binary Classification ◽

Disease Diagnosis ◽

Feature Descriptors ◽

Histopathological Images ◽

Viable Approach ◽

Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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Classiﬁcation of Breast Cancer Histopathology Images by Cell-Centered Deep Learning Approach

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302066 ◽

2020 ◽

Author(s):

Emre Egriboz ◽

Berkay Gokcen ◽

Gokhan Bilgin

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Learning Approach ◽

Histopathology Images

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Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images

Cancers ◽

10.3390/cancers13102419 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2419

Author(s):

Georg Steinbuss ◽

Mark Kriegsmann ◽

Christiane Zgorzelski ◽

Alexander Brobeil ◽

Benjamin Goeppert ◽

...

Keyword(s):

Deep Learning ◽

Hodgkin Lymphoma ◽

Expert Knowledge ◽

B Cell Lymphoma ◽

High Accuracy ◽

Lymphocytic Leukemia ◽

Non Hodgkin Lymphoma ◽

Image Patches ◽

Histopathological Images

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

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