Computational Intelligence and Neuroscience / 2022 / Article / Tab 1 / Research Article
[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%