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.

Breast cancer remains the most common type of cancer and the leading cause of cancer-induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths per year. Historically, a diagnosis has been initially performed using clinical screening followed by histopathological analysis. Automated classification of cancers using histopathological images is a chciteallenging task of accurate detection of tumor sub-types. This process could be facilitated by machine learning approaches, which may be more reliable and economical compared to conventional methods. To prove this principle, we applied fine-tuned pre-trained deep neural networks.