Deep Learning Model with Enhanced Segmentation and Combined Feature Activation for Mitosis Classification

Title

Deep Learning Model with Enhanced Segmentation and Combined Feature Activation for Mitosis Classification

Creator

Lijo, Jithy; Saleema, J.S.; Babu, Tina

Description

Mitosis is a cell division mechanism vital for the growth of tissues and repair, Histopathological images are used by pathologists to diagnose cancer, but mitosis classification plays an important role in disease diagnosis. The mitotic counts are a proliferative indicator to find the aggressiveness of breast cancer. Detecting the mitotic tumor cells in tissue areas is a critical marker in cancer prognosis. Various researchers have focused on developing an automatic detection framework to identify mitotic figures, but detecting and classifying mitosis accurately remains a significant challenge in the medical field. Moreover, this research has designed a proposed Aggressive Tracing Seeking Optimization (ATSO) based Deep Convolutional Neural Network (Deep CNN) for the mitosis classification framework. The proposed framework uses less memory and increases the convergence rate; hence, it is globally efficient in achieving optimal solutions in the search space. The inspiration for considering the ATSO is its excellent behavior, as well as its scalable and adaptable mechanism, which allows optimization to move away from local optima. Moreover, it is computationally faster and exhibits higher global convergence capability in searching for the best solution. ATSO optimally trains a Deep CNN to generate higher classification accuracy by minimizing the false rate using the loss function. More explicitly, the proposed ATSO-Deep CNN model attained higher performance with an accuracy of 96.31%, an F1-score of 96.3%, precision of 96.84%, and recall of 95.78% with a 90% training percentage for the BreCaHAD dataset. 2025 Inventive Research Organization.

Source

Journal of Innovative Image Processing;Volume;7;Issue;4;pp.1186-1211

Date

01-01-2025

Publisher

Inventive Research Organization

Subject

Cancer Prognosis; Deep Convolutional Neural Network; Feature Fusion; Histopathological Images; Mitosis Classification; Segmentation

Coverage

Lijo J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India, School of Computer Applications, Dayanada Sagar University, Karnataka, Bengaluru, India; Saleema J.S., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India; Babu T., Alliance School of Advanced Computing, Alliance University, Karnataka, Bengaluru, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 25824252;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.36548/jiip.2025.4.006

https://www.scopus.com/pages/publications/105020267162?origin=resultslist