# Quadra Sense: A Fusion of Deep Learning Classifiers for Mitosis Detection in Breast Cancer Histopathology

## TL;DR

This paper introduces Quadra Sense, a deep learning method that improves mitosis detection in breast cancer histopathology images, achieving high accuracy.

## Contribution

Quadra Sense combines multiple deep learning techniques and optimization algorithms to enhance mitosis detection accuracy in breast cancer.

## Key findings

- Quadra Sense achieved 99.2% accuracy in mitosis detection.
- The method outperformed existing approaches in performance evaluation.
- The fusion of normalization, feature extraction, and optimization algorithms improved detection reliability.

## Abstract

Background/Objectives: The difficulties caused by breast cancer have been addressed in a number of ways. Since it is said to be the second most common cause of death from cancer among women, early intervention is crucial. Early detection is difficult because of the existing detection tools’ shortcomings in objectivity and accuracy. Quadra Sense, a fusion of deep learning (DL) classifiers for mitosis detection in breast cancer histopathology, is proposed to address the shortcomings of current approaches. It demonstrates a greater capacity to produce more accurate results. Methods: Initially, the raw dataset is preprocessed by using a normalization by means of color channel normalization (zero-mean normalization) and stain normalization (Macenko Stain Normalization), and the artifact can be removed via median filtering and contrast enhancement using histogram equalization; ROI identification is performed using modified Fully Convolutional Networks (FCNs) followed by the feature extraction (FE) with Modified InceptionV4 (M-IV4), by which the deep features are retrieved and the feature are selected by means of a Self-Improved Seagull Optimization Algorithm (SA-SOA), and finally, classification is performed using Mito-Quartet. Results: Ultimately, using a performance evaluation, the suggested approach achieved a higher accuracy of 99.2% in comparison with the current methods. Conclusions: From the outcomes, the recommended technique performs well.