Breast Cancer Classification: A Deep Learning Approach for Digital Pathology

2019 ◽  
pp. 33-40 ◽  
Author(s):  
Pablo Guillén-Rondon ◽  
Melvin Robinson ◽  
Jerry Ebalunode
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Digital Pathology ◽  
Cancer Classification ◽  
Learning Approach ◽  
Breast Cancer Classification

scholarly journals Novel breast cancer classification framework based on deep learning

2020 ◽  
Vol 14 (13) ◽  
pp. 3254-3259
Author(s):  
Wessam M. Salama ◽  
Azza M. Elbagoury ◽  
Moustafa H. Aly
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Classification Framework

scholarly journals Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 187531-187552
Author(s):  
Faezehsadat Shahidi ◽  
Salwani Mohd Daud ◽  
Hafiza Abas ◽  
Noor Azurati Ahmad ◽  
Nurazean Maarop
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Classification ◽  
Learning Approaches ◽  
Comparison Study ◽  
Breast Cancer Classification

Breast Cancer Classification using Discrete Wavelet Transformation and Deep Learning

2019 ◽  
Vol 13 ◽  
Author(s):  
Emmanuel Masa-Ibi ◽  
Rajesh Prasad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Cancer Classification ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
Cancer Data ◽  
Breast Cancer Classification ◽  
The Neural Network

Background: One of the most prevalent sicknesses these days is breast cancer which is common amongst women. This sickness has been in increase to an alarming rate due to the lack of accurate administration of diagnoses. Early and accurate detection is one of the safest ways to cure a breast cancer patient. Objectives: The objective of this study is to proffer a more effective way to accurately classify a cancer sample; whether is Benign or Malignant. Methods: The classification model is based on the data collected from the UCI machine learning repository acquired from Wisconsin hospital called Wisconsin breast cancer data (WBCD). In this study, we preprocessed the dataset using DWT and then test the efficiency of deep learning (DL) for breast cancer classification. The model is developed using a feed-forward neural network and the result is compared with the observed values. Results: The result of the experiment proved the effectiveness of the proposed classification technique. The new technique accomplishes 98.90% accuracy for classifying breast cancer. Conclusions: The result from the experiment shows that the importance of data preprocessing and the efficiency of the neural network over other classification algorithms.

Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images

2019 ◽  
Vol 50 (4) ◽  
pp. 1144-1151 ◽  
Author(s):  
Juan Zhou ◽  
Lu‐Yang Luo ◽  
Qi Dou ◽  
Hao Chen ◽  
Cheng Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Classification ◽  
Mr Images ◽  
Breast Cancer Classification ◽  
Weakly Supervised

scholarly journals Breast Cancer Classification from Histopathological Images using Patch-based Deep Learning Modeling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Irum Hirra ◽  
Mubashir Ahmad ◽  
Ayaz Hussain ◽  
M. Usman Ashraf ◽  
Iftikhar Ahmed Saeed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Histopathological Images

High-level Features in Deeper Deep Learning Layers for Breast Cancer Classification

2021 ◽  
Author(s):  
Noor Fadzilah Razali ◽  
Iza Sazanita Isa ◽  
Siti Noraini Sulaiman ◽  
Noor Khairiah A. Karim ◽  
Muhammad Khusairi Osman
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
High Level
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