LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
- Title
- LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
- Creator
- Lijo J.; Janardhanan Subramanian S.
- Description
- Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
- Source
- International Journal of Information Technology (Singapore), Vol-16, No. 6, pp. 3517-3536.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media B.V.
- Subject
- Biwolf optimization; Feature extraction; LP Norm Regularized deep CNN; Pre-processing; Yolo V5
- Coverage
- Lijo J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India, Department of Computer Science and Applications, Christ Academy Institute for Advanced Studies, Karnataka, Bengaluru, India; Janardhanan Subramanian S., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 25112104
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Lijo J.; Janardhanan Subramanian S., “LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 2, 2025, https://archives.christuniversity.in/items/show/12990.