A new automated segmentation and classification of mammogram images

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

“…From these tables we can infer that a customized CNN is the most common approach used for the classification of breast density. (Gargouri et al, 2022;Malathi and Latha, 2023;Dafni Rose et al, 2022;Saber et al, 2023;Abdelhafiz et al, 2020) (Patil and Biradar, 2021;Patil et al, 2022;Ragab et al, 2021) Beginning with seed points, it gradually expands regions by incorporating neighboring pixels that share specific similarity criteria, like intensity or texture. The method continues until the termination conditions are satisfied, which ensures that the region reaches the desired size or meets a boundary.…”

“…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.…”