Is mass classification in mammograms a solved problem? - A critical review over the last 20 years

Author:

Pedro Ricardo Wandré Dias,Machado-Lima Ariane,Nunes Fátima L.S.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference106 articles.

1. The impact of pixel resolution, integration scale, preprocessing, and feature normalization on texture analysis for mass classification in mammograms;Abdel-Nasser;International Journal of Optics,2016

2. Mammogram image visual enhancement, mass segmentation and classification;Al-Najdawi;Applied Soft Compututing,2015

3. Mass classification method in mammogram using fuzzy k-nearest neighbour equality;Aroquiaraj;CoRR,2014

4. Fuzzy morphological extreme learning machines to detect and classify masses in mammograms;Azevedo,2015

5. Morphological extreme learning machines applied to detect and classify masses in mammograms;Azevedo,2015

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1. An optimized ensemble classifier for mammographic mass classification;Computers and Electrical Engineering;2024-10

2. Practical Application to Interpretable Medical Risk Analysis;Fuzzy Rule-Based Inference;2024

3. Extended Mammogram Classification From Textural Features;Studia Universitatis Babeș-Bolyai Informatica;2023-06-02

4. A Stochastic Grammar Approach to Mass Classification in Mammograms;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2023-05-01

5. Anchor-free YOLOv3 for mass detection in mammogram;Expert Systems with Applications;2022-04

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