[Retracted] CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning

Table 1

Summary of research work about breast cancer diagnosis using machine learning methods.

Author	Year	Dataset	Image type	Feature extraction	Classification	Accuracy
Patil & Biradar [28]	2021	MIAS	Mammography	Gray level co-occurrence matrix	Convolutional neural network, recurrent neural network	98.3%
Masud et al. [29]	2021	Rodrigues	Ultrasound	Image	Convolutional neural networks	—
Chung et al. [30]	2021	EHR	Ultrasound	—	Statistical method	—
Fei et al. [14]	2021	Nanjing drum tower hospital	B-mode and elastography ultrasound	Gray level co-occurrence matrix	Doubly supervised parameter transfer classifier	86.73%
Muduli et al. [10]	2020	MIAS	Mammography	Lifting wavelet transform	Extreme learning machine	94.76%
Melekoodappattu et al. [11]	2020	MIAS	Mammography	Gray level co-occurrence matrix	Fruit fly optimization algorithm and extreme learning machine	97.5%
Sasikala et al. [12]	2020	DDSM and INbreast	Mammography	Local binary pattern	Binary firefly approach with optimum-path forest classifier	98.56%
Begum et al. [13]	2020	MIAS	Mammography	Image	Optimal wavelet statistical texture and recurrent neural network	96.43%
Khandezamin et al. [31]	2020	WBCD, WDBC, WPBC	Digitized image of a fine needle aspirate	Logistic regression	Group method data handling neural network	99.4%
Vo et al. [32]	2019	Bioimaging 2015, BreaKHis	Histopathology	Image	Incremental boosting convolution networks	96.45%