Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Author:

Abbasniya Mohammad RezaORCID,Sheikholeslamzadeh Sayed AliORCID,Nasiri HamidORCID,Emami SamanehORCID

Funder

University of Tehran

Luleå Tekniska Universitet

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science,Control and Systems Engineering

Reference30 articles.

1. Combination of loss functions for robust breast cancer prediction;Hajiabadi;Comput Electr Eng,2020

2. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images;Mohammed;Comput Electr Eng,2018

3. An automated breast cancer diagnosis using feature selection and parameter optimization in ANN;Punitha;Comput Electr Eng,2021

4. Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images;Alirezazadeh;Biocybern Biomed Eng,2018

5. Automatic classification of tissue malignancy for breast carcinoma diagnosis;Fondón;Comput Biol Med,2018

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