แจ้งเอกสารไม่ครบถ้วน, ไม่ตรงกับชื่อเรื่อง หรือมีข้อผิดพลาดเกี่ยวกับเอกสาร ติดต่อที่นี่ ==>
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
หากไม่มีอีเมลผู้รับให้กรอก thailis-noc@uni.net.th
Pandya, Rahul J.. A comparative study of deep learning architectures for cervical cell segmentation in Pap smear images. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2025.
A comparative study of deep learning architectures for cervical cell segmentation in Pap smear images
Name: Palaparthi, Revanth
Organization :
IIT Dharwad. Dept. of CSE
Email :
210010043@iitdh.ac.in
Name: Chinta, Sahitya
Organization :
IIT Dharwad. Dept. of CSE
Email :
210010045@iitdh.ac.in
Name: Samad, Shaik Abdul
Organization :
IIT Dharwad. Dept. of EECE
Email :
ee23mt018@iitdh.ac.in
Name: Pandya, Rahul J.
Organization :
IIT Dharwad. Dept. of EECE
Email :
rpandya@iitdh.ac.in
LCSH:
Image segmentation
LCSH:
Diagnostic imaging
Abstract:
Segmentation of cervical cells is an essential step in the early detection and diagnosis of cervical cancer. This study proposes a comprehensive deep learning pipeline for binary segmentation of cervical cells in Pap smear images based on eight advanced and recent architectures: U-Net, Attention U-Net, U-Net++, DeepLabv3+, PAN, SegFormer, YOLOv8, and a self-designed YOLOv11.
The models were trained and tested using the publicly available SIPaKMeD dataset, which contains 966 high-resolution labeled cytology images. All images were divided into overlapping 512 × 512 patches, generating over 8,000 training patches and 1,208 testing patches. Data augmentation techniquesincluding flipping, rotation, elastic deformation, and contrast adjustmentwere applied to increase variability.
Segmentation performance was quantitatively evaluated using six metrics: Dice coefficient, Intersection over Union (IoU), precision, recall, F1-score, and accuracy. The U-Net++ model achieved the highest performance with a Dice score of 0.8366 and an IoU score of 0.7771. The YOLOv11 model demonstrated competitive performance with a Dice score of 0.8349 and an IoU of 0.7453, while providing reduced inference time.
These findings highlight the trade-offs between segmentation accuracy and computational speed, and indicate that the proposed pipeline has potential for integration into real-time clinical diagnostic systems.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2025
Modified:
2026-02-24
Issued:
2026-02-24
บทความ/Article
application/pdf
BibliograpyCitation :
In Prince of Songkla University, Phuket Campus. College of Computing. The 9th International Conference on Information Technology (InCIT 2025) (pp.176-182). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
-0.950751 วินาที
Palaparthi, Revanth
| Title | Contributor | Type |
|---|---|---|
| A comparative study of deep learning architectures for cervical cell segmentation in Pap smear images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Palaparthi, Revanth;Chinta, Sahitya;Samad, Shaik Abdul;Pandya, Rahul J. | บทความ/Article |
Chinta, Sahitya
| Title | Contributor | Type |
|---|---|---|
| A comparative study of deep learning architectures for cervical cell segmentation in Pap smear images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Palaparthi, Revanth;Chinta, Sahitya;Samad, Shaik Abdul;Pandya, Rahul J. | บทความ/Article |
Samad, Shaik Abdul
| Title | Contributor | Type |
|---|---|---|
| A comparative study of deep learning architectures for cervical cell segmentation in Pap smear images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Palaparthi, Revanth;Chinta, Sahitya;Samad, Shaik Abdul;Pandya, Rahul J. | บทความ/Article |
Pandya, Rahul J.
| Title | Contributor | Type |
|---|---|---|
| A comparative study of deep learning architectures for cervical cell segmentation in Pap smear images
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Palaparthi, Revanth;Chinta, Sahitya;Samad, Shaik Abdul;Pandya, Rahul J. | บทความ/Article |