Optimized generative adversarial network based breast cancer diagnosis with wavelet and texture features

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

“…Apart from the TL models discussed, there are a few recent studies that have used dual-view CNN (AlGhamdi and Abdel-Mottaleb, 2021;Wu et al, 2019), multi-task CNN (Gao et al, 2020;Wimmer et al, 2021). In addition, new techniques have emerged in the latest studies, such as attention based CNNs which use the attention block to assist the network in focusing on discriminating the characteristics of images (Al-Hejri et al, 2022;Songsaeng et al, 2021;Wang et al, 2022;Sun et al, 2023;Hina et al, 2020;Zhang et al, 2020), Generative Adversarial Networks Deep Adversarial Domain adaptation (Wang et al, 2021a) which uses domain adaptation for classification, Generative Adversarial Network in which the generator generates realistic images and the discriminator differentiates between real and synthetic data, along with performing classification tasks (Park et al, 2023;Ponraj and Canessane, 2023b;Shivhare and Saxena, 2022), and Student teacher InterNRL (Wang et al, 2023) in which the student serves as a prototype-based classifier and teacher represents a image classifier. Auto Encoders (Hamza, 2023) trained on labeled data and contributed to unsupervised feature learning and supervised classification, while Visual Transformers (ViT) (Chen et al, 2022;Xia et al, 2023;Prodan et al, 2023) utilize a transformer-style architecture across patches of the image, and semi-supervised (Calderon-Ramirez et al, 2022) were also used to overcome the missing annotation problem.…”

“…Inception-v3 improved the model by using factorised convolutions and dimension reductions. (Babu and Jerome, 2022;Mahmood et al, 2022;Shen et al, 2020;Wang et al, 2019;Zhang et al, 2021a), (Albalawi et al, 2022;Cai et al, 2021;Chandraraju and Jeyaprakash, 2022;Gargouri et al, 2022;George Melekoodappattu et al, 2022;Jones et al, 2023;Kurek et al, 2018;Mahmoud et al, 2021;Mokni and Haoues, 2022;Patil and Biradar, 2021;Patil et al, 2022;Razali et al, 2023a;Sajid et al, 2023;Shivhare and Saxena, 2022;Arputham et al, 2021;Chouhan et al, 2021;Chugh et al, 2022;Ramesh et al, 2022;Sivakrithika and Dinakaran, 2018;Ranjbarzadeh et al, 2023a;Song et al, 2020;Yu et al, 2023c;Elmoufidi, 2022;Hosni Mahmoud et al, 2022) (Babu andJerome, 2022), (Razali et al, 2023a;Arputham et al, 2021;Chouhan et al, 2021) (Mahmood et al, 2022;Shen et al, 2020;Wang et al, 2019;Zhang et al, 2021a), (Albalawi et al, 2022;Razali e...…”

“…The relationship between metric learning layers and traditional CNN structures has been described as parasitism, in which one species (metric learning layers) benefits at the expense of the other (CNN structures). Visual Transformers (ViT) (Kumbhare et al, 2023;Li et al, 2021b;Patil and Biradar, 2021;Patil et al, 2022;Sakthivel et al, 2022a;Ashwini et al, 2023) (Park et al, 2023), (Ponraj and Canessane, 2023b;Shivhare and Saxena, 2022) (Xia et al, 2023), (Prodan et al, 2023;Chen et al, 2022) RNNs use recurrent connections to retain past information. This architecture allows sequential data processing, which makes RNNs particularly effective.…”

Deep Learning Theories and Methods for Breast Cancer Classification

“…Generative Adversarial Networks (GAN [24]) are primarily known for their ability to generate realistic data, but they can also play a role in improving breast cancer detection through various innovative approaches [60,61]. GAN can be used to augment medical imaging datasets, including those used for breast cancer detection.…”