A lightweight deep learning method to identify different types of cervical cancer
Md Humaion Kabir Mehedi, Moumita Khandaker, Shaneen Ara, Md Ashraful Alam, M F Mridha, Zeyar Aung · 2024
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Description
Md Humaion Kabir Mehedi and five co-authors build a light deep learning model to sort cervical cancer types in a 2024 paper. This cancer is the second most common in women and grows from an early, treatable stage of abnormal cells, mostly after HPV infection. Fast, accurate sorting of cell images can speed screening where pathologists are scarce.
Their model, CCanNet, stacks squeeze blocks, residual blocks, and skip links in a lean frame. It was trained on the public SipakMed data set of cervical cell images. The team compared it against well-known image models and transformer designs, including several VGG and mobile networks. A lean model that matches heavy ones can run on modest clinic hardware.
Catalog record
| CREATOR | Md Humaion Kabir Mehedi, Moumita Khandaker, Shaneen Ara, Md Ashraful Alam, M F Mridha, Zeyar Aung |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 61 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |