Exploring subcellular location anomalies: a novel quantum bioimaging paradigm

The complex world of cellular dynamics has never been easier to understand, because of the developments in Bio imaging technology. When it comes to precisely investigating subcellular structures, traditional imaging techniques are useful but sometimes ithave drawbacks. In this work, we provide a novel Quantum Bioimaging Paradigm (QBP) intended to resolve subcellular localization anomalies at a resolution until thought to be impossible.Subcellular abnormalities in complex biological structures are disgracefully difficult for conventional tools to capitalize on properly. A novel approach is called Improved Quantum Convolutional Neural Network (IQ-CNN), which is important to use feature selection methods to ensure that the features are optimal. Anomaly detection relies on identifying and retaining important characteristics. Collecting the dataset contains Human Protein Atlas (HPA), and immunohistochemistry (IHC) microscopy images for training and validation. We employ Decimal Scaling Normalization to standardize pixel values, ensuring consistent representation across diverse Bioimaging datasets. Extract the feature from the normalized Bioimaging dataset using the Histogram of Oriented Gradients (HOG) method. Our investigations assess the suggested model’s efficacy by contrasting the IQ-CNN with other methods. Accuracy (97%), precision (87%), recall (82%), and F1-score (95%), among other assessments, are used. The suggested method shows excellent results in terms of enhanced subcellular anomaly detection reliability. These outcomes are improvement advance for quantum-enhanced Bioimaging, a rapidly developing area with promising potential for use in medical diagnostics and treatment.