Image Dataset for Cervical Cell Diagnosis – a Review-Reference-Cited by-同舟云学术

Image Dataset for Cervical Cell Diagnosis – a Review

Published:2023-07-15 Issue: Volume: Page:
ISSN:
Container-title:2023 6th International Conference on Engineering Technology and its Applications (IICETA)
language:
Short-container-title:

Author:

Alias Nur Ain¹,Mustafa Wan Azani¹,Jamlos Mohd Aminuddin²,Alquran Hiam²,Ghani Miharaini Md³,Hanafi Hafizul Fahri⁴,Lah Noor Hidayah Che⁴,Ismail Shahrina⁵,Mohammed F. F.⁶,Alkhayyat Ahmed⁷

Affiliation:

1. Universiti Malaysia Perlis UniCITI,Faculty of Electrical Engineering Technology,Padang Besar,Perlis,Malaysia,02100

2. Universiti Malaysia Perlis, UniCITI,Faculty of Electronic Engineering Technology,Padang Besar,Perlis,Malaysia,02100

3. Universiti Putra Malaysia,Faculty of Modern Language and Communication,Department of Communication,Malaysia

4. Universiti Pendidikan Sultan Idris,Faculty of Art, Computing and Creative Industry,Computing Department,Tanjong Malim,Perak,Malaysia,35900

5. Universiti Sains Islam Malaysia (USIM),Faculty of Science and Technology,Negeri Sembilan,Malaysia,71800

6. Al-Zahraa University for Women,Karbala,Iraq

7. The Islamic University,Faculty of Engineering,Najaf,Iraq

Funder

ministry of higher education (MOHE) Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10351190/10351191/10351193.pdf?arnumber=10351193

Reference30 articles.

1. Deep multiple-instance learning for abnormal cell detection in cervical histopathology images

2. Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study

3. DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques

4. Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm

5. Detection and Classification of Cervical Exfoliated Cells Based on Faster R-CNN*