Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
- Title
- Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
- Creator
- Lijo J.; Saleema J.S.
- Description
- One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. 2024 IEEE.
- Source
- 2024 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Augmentation; deep learning; histopathological images; mitosis detection; stain normalization
- Coverage
- Lijo J., Research Scholar, Christ (Deemed to Be University), Karnataka, Bengaluru, India, Christ Academy Institute for Advanced Studies, Karnataka, Bengaluru, India; Saleema J.S., Christ Deemed to Be University, Department of Computer Science, Karnataka, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035084-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Lijo J.; Saleema J.S., “Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed November 4, 2025, https://archives.christuniversity.in/items/show/19428.