Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation
Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen · 2024
Read Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation by Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11585584.txt.
Description
Gunjan Shandilya and co-authors build a smarter screen for cervical cancer cells in a 2024 paper on medical image classification. Catching abnormal cells early transforms survival odds, but reading microscope images by hand is slow, and subtle patterns are easy to miss.
Their convolutional network stacks three layers of growing filters with pooling, dropout, and dense layers, and borrows skip connections from ResNet designs so fine detail survives to the decision layers. Synthetic images from a generative adversarial network expand the training set. The authors report sharper recognition of the faint features that separate healthy cells from cancerous ones, aiming at faster, more reliable screening.
Catalog record
| CREATOR | Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 62 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |