A Novel Approach to Breast Cancer Histopathological Image Classification
  Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble
  Method
Sambit Mallick Snigdha Paul Anindya Sen
Breast cancer classification stands as a pivotal pillar in ensuring timely
diagnosis and effective treatment. This study with histopathological images
underscores the profound significance of harnessing the synergistic
capabilities of colour space ensembling and quantum-classical stacking to
elevate the precision of breast cancer classification. By delving into the
distinct colour spaces of RGB, HSV and CIE L*u*v, the authors initiated a
comprehensive investigation guided by advanced methodologies. Employing the
DenseNet121 architecture for feature extraction the authors have capitalized on
the robustness of Random Forest, SVM, QSVC, and VQC classifiers. This research
encompasses a unique feature fusion technique within the colour space ensemble.
This approach not only deepens our comprehension of breast cancer
classification but also marks a milestone in personalized medical assessment.
The amalgamation of quantum and classical classifiers through stacking emerges
as a potent catalyst, effectively mitigating the inherent constraints of
individual classifiers, paving a robust path towards more dependable and
refined breast cancer identification. Through rigorous experimentation and
meticulous analysis, fusion of colour spaces like RGB with HSV and RGB with CIE
L*u*v, presents an classification accuracy, nearing the value of unity. This
underscores the transformative potential of our approach, where the fusion of
diverse colour spaces and the synergy of quantum and classical realms converge
to establish a new horizon in medical diagnostics. Thus the implications of
this research extend across medical disciplines, offering promising avenues for
advancing diagnostic accuracy and treatment efficacy.