A Quantum Convolutional Neural Network for Breast Cancer Classification using Boruta and GA-Based Feature Selection with Quantum Feature Maps

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

- We present a comprehensive study exploring quantum machine learning (QML) approaches for plant disease detection from leaf images and compare them against well-established classical counterparts. Specifically, we implement and analyze Quantum Support Vector Machines (QSVMs) vs classical SVMs, and Quantum Convolutional Neural Networks (QCNNs) vs classical CNNs. Using the widely used PlantVillage and complementary field datasets, we describe image preprocessing, classical baseline architectures, quantum data-encoding strategies, circuit-level QSVM and QCNN designs for near-term quantum devices, and hybrid training procedures. Where possible, we review literature-reported performance and propose a reproducible experimental pipeline for empirical evaluation on simulated/noisy quantum backends. We discuss expected strengths and limitations of quantum approaches (expressivity, kernel advantages, resource constraints), provide detailed evaluation metrics and ablations, and propose directions for real-device experiments and field deployment. Key takeaways: QSVM/quantum-kernel methods can provide superior separability on certain feature maps and small-to-medium-sized datasets, while QCNNs show promise as compact feature extractors for hybrid pipelines — but both approaches currently require careful circuit design and error-mitigation to outperform well-tuned classical models in realistic field settings. Key Words: QCNN, Plant Disease, SVM, CNN, QSVM

Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( >1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL.

Breast cancer is a kind of malignant tumor that mostly found in women which grows around the tissue of breast. During the process of diagnosis the cancer cells and non cancer cells are been identified and their count is made. If the count of cancer cells is greater than non cancer cells then the patient undergoes the treatments. The early detection of breast cancer can save the life of patient. Now a day's AI is playing a vital role in classifying cancer and non cancer cells in a accurate way. Many of the earlier researchers had performed the work of classifying them but, most of them lack in the accuracy. To have good accuracy a new method EBCS (Early Breast Cancer Screening) is proposed which consists of four steps or phases- preprocessing, classifying, performance evaluation and providing clear understanding to human.. To implement the four steps python programming is been used and cancer data set was extracted from Breast Cancer Wisconsin (Diagnostic) data set. The proposed method EBCS gave an accuracy of 99 percentage and complexity is also reduced.

Abstract— Breast cancer remains one of the leading causes of mortality among women worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Traditional machine learning models often struggle to maintain high classification accuracy due to data variability and the complex nature of tumor characteristics. This study proposes an adaptive voting ensemble learning algorithm to enhance breast cancer classification performance. The ensemble integrates multiple classifiers—such as Decision Trees, Support Vector Machines, and K-Nearest Neighbors—by assigning dynamic weights based on each model's real-time performance. The algorithm is evaluated using the Wisconsin Breast Cancer Dataset (WBCD), and its effectiveness is measured against individual classifiers using metrics like accuracy, precision, recall, and F1-score. Results demonstrate that the adaptive ensemble significantly outperforms standalone models, offering a more robust and reliable approach for breast cancer prediction. This method shows promise for application in clinical decision support systems, contributing to more accurate diagnostics and better treatment planning. Keywords- Breast Cancer Classification, Adaptive Voting, Ensemble Learning, Machine Learning, Wisconsin Breast Cancer Dataset (WBCD), Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Medical Diagnosis, Predictive Modeling

Breast cancer and prostate cancer, the most common cancers in American women and men, respectively, are major public health concerns. Investigations into the prevention and early detection of breast and prostate cancer include research on the potential impact of diet, particularly dietary fat, on breast cancer risk; the chemoprevention of breast cancer using tamoxifen and N-(4-hydroxyphenyl)retinamide(4-HPR); the importance of mammography in early detection of breast cancer; the potential of finasteride in preventing or retarding the progression of early-stage prostate cancer; and improved detection of prostate cancer by combining various screening modalities. The importance of transferring information gained though such research to the health care community cannot be overemphasized. Further, it is critical that cancer prevention and detection education be built into the curricula of leading medical and biomedical institutions. Education is key to developing a strong and vigorous cancer prevention and early detection research program.

DNA ploidy and cell kinetics determined by flow-cytometric (FCM) analysis have provided relevant information on the natural history of breast cancer. These highly feasible and evaluable assays are particularly appropriate for small samples from fine-needle aspirates (FNA) to complement cytologic information for diagnosis of breast lesions. DNA content and the percentage of cells in the cell cycle phases were evaluated on FCM DNA histograms from 281 consecutive FNA of benign and malignant breast lesions. FCM analysis was possible in 93% of FNA. Qualitative concordance in DNA content between FNA and surgical specimens was observed in 82% of the patients. Of the 145 aneuploid FNA, 144 (99%) were histologically diagnosed as carcinomas. The presence of only diploid clones was observed in 115 FNA: rapidly proliferating (S+G2M > 12%) diploid lesions proved to be histologically malignant in 67% of patients, and slowly proliferating diploid lesions were histologically benign in 79% of patients. False-positive results of ploidy and cell kinetics were observed in 1 and 10 patients, respectively. Of the 163 cancers evaluable for cytology and FCM, 158 were positive at cytology. The presence of aneuploid or of diploid rapidly proliferating clones in cytologically false-negative aspirates resulted in the detection of four additional cancers. Flow cytometric analysis of ploidy and cell kinetics on FNA could be a valuable tool complementary to cytology for breast cancer diagnosis.

Introduction: The use of social media by healthcare professionals to create disease awareness has been increased worldwide. It is easily available and accessible to the larger number of populations and hence helps in bridging the gap between doctor and patient. Breast cancer is the leading cause of cancer among women in India and the main reason for higher mortality among them is the late presentation to the surgeons, owing to various myths and social and physical taboos. We conducted a study to assess the awareness of breast cancer among the women in general population and also to check the effectiveness of WhatsApp in improving women's knowledge regarding the risk reduction, myths, early detection and treatment of breast cancer. The study was a pre-post health educational study including 146 wom Methods: en. Multiple WhatsApp groups were created to share information regarding breast cancer awareness, early detection, risk factors, common myths, self-breast examination and treatment of breast cancer in the form of audio, video, text and images. The group was active for 4-5 days as an interactive session. Data were collected through google forms. We noticed a signicant improvement in the knowledge of women regard Results: ing the common myths regarding breast cancer and the symptoms of breast cancer. There was signicant increase in performance of self-breast examination from 14.4% to 84.6% (p- <0.001). Most of the women found the video of self-breast examination useful. The commonly Conclusion: used social networking site like WhatsApp can be used as a tool to assess the awareness of breast cancer among women. This also helped in exchange of the knowledge among women, which in turn helped in the risk reduction and early detection of breast cancer. This app is a useful tool that can be incorporated into health education strategies that focus on breast cancer.

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care.

Breast cancer remains one of the leading causes of mortality worldwide, underscoring the need for timely and accurate diagnosis to facilitate effective treatment. This study explores the application of machine learning based feature selection techniques to improve the identification of breast cancer using the Wisconsin Breast Cancer Dataset. The dataset comprises 569 instances and 32 attributes. After removing one non-informative attribute, the final dataset retained 30 features and one target label. The target variable, originally labeled as malignant or benign was converted into a binary format (1 for malignant and 0 for benign). To enhance model interpretability and reduce feature redundancy, three feature selection algorithms Relief, LASSO, and Fast Conditional Mutual Information (FCMIM) were employed to identify the most relevant features. An Artificial Neural Network (ANN) classifier was trained using the selected features to differentiate between malignant and benign cases. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the Matthews Correlation Coefficient (MCC). The results demonstrate that feature selection significantly improves classification performance and computational efficiency. This framework underscores the potential of machine learning in oncology, offering a scalable and accurate solution for early breast cancer detection and enhanced diagnostic decision-making.

Studies of the etiology, early detection, and prevention of breast cancer reported in the past year are reviewed in this paper. Studies of the etiology of breast cancer include reports on genetic and environmental factors. A major advance in the study of inherited forms of breast and ovarian cancer occurred with the identification of the BRCA1 gene. A second breast cancer susceptibility gene, the BRCA2 gene, was localized to chromosome 13q12-13. Multiple mutations in the BRCA1 gene have been identified, presenting a challenge for the development of predictive testing. Controversy continues over the association between hormone replacement therapy and the development of breast cancer. A study of exercise suggests a strong protective effect against the development of early onset breast cancer. Recent studies have failed to find a strong link between dietary fat intake and the development of breast cancer. A meta-analysis of studies of the efficacy of screening for the prevention of breast cancer mortality demonstrates a significant reduction in mortality among women 50 years of age and older. A lowering of breast cancer mortality for women aged 40 to 49 was only demonstrated after 10 to 12 years of follow-up. The risks and benefits of tamoxifen therapy, a potential breast cancer chemoprevention agent, continue to be clarified. Adverse effects on the endometrium, including an increased risk of endometrial cancer, have been reported. Beneficial effects include an improved cardiovascular risk profile and preservation of bone mineral density among postmenopausal women.

The sector that affects all the citizens as a common factor is the health sector. It has huge data and important information about patients and their health. Now it is the need of the hour to utilize those enormous data and to extract knowledge, which will be useful to all the stake holders related to electronic health records. In this paper, we have focused on feature selection techniques to gain high quality attributes to enhance the mining process. Feature selection techniques touch all sectors, which require knowledge discovery. In this study, we have made a comparison between different feature selection methods and associated machine learning algorithms on Wisconsin Breast Cancer dataset, Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) of UCI Repository. The study found that fusion of classification algorithms perform better on WBCD. It also revealed that IG performs better on WBCD, whereas IG and CFS give good results on WPBC, and CA gives best results on WDBC.

Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing’s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this research work. This paper explores the integration of QML into anomaly detection systems for cyber security audits, where detecting deviations from normal behavior is crucial. Quantum algorithms, particularly those based on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum Principal Component Analysis (QPCA) can enhance the detection of subtle anomalies that classical algorithms may overlook due to noise or the complex, high-dimensional nature of cyber data. The inherent properties of quantum computing, such as superposition and entanglement, allow for more efficient feature selection and optimization, potentially leading to faster and more accurate anomaly detection. The impact of implementing QML in cyber security audits is profound. First, it enhances detection capabilities by identifying anomalies with greater precision, reducing false positives, and improving response times to cyber incidents. Second, quantum algorithms’ ability to manage exponentially large datasets makes them ideal for environments with extensive data logs, such as enterprise networks and cloud infrastructures. Third, as cyber threats become increasingly adaptive and stealthy, QML offers a dynamic solution that evolves alongside these threats by continuously learning from new patterns of attack. However, practical challenges remain, including the need for quantum hardware advancements, the development of hybrid quantum-classical models, and ensuring the interpretability of quantum models in audit scenarios. Despite these challenges, early research and experimental implementations demonstrate the potential of QML to revolutionize anomaly detection in cybersecurity audits. This paper concludes that while QML is still in its early stages, its application to anomaly detection holds promise for significantly enhancing the effectiveness of cyber security audits. The impact of this technology, when fully realized, could redefine how organizations protect their networks and data from ever-evolving cyber threats, making QML a critical area for future research and development in cybersecurity.

Breast cancer classification remains a critical challenge in medical diagnostics due to the imbalanced nature of available datasets, where the minority (cancerous malignant) class is often overshadowed by the majority (benign) class. This study proposes a hybrid model based on logistic regression, enhanced with class balancing techniques and ant search optimization, to improve the identification of the malignant class. The model is compared with SVM, Random Forest, and KNearest Neighbors (KNN) across three stages: prediction before diagnosis, at diagnosis and therapy, and post-treatment outcomes. The experiments, conducted on the Jupyter platform using the Wisconsin breast cancer dataset, demonstrate that the hybrid model achieves a high accuracy of 92.98%, significantly reducing false negatives. The study highlights the strengths of logistic regression in providing interpretable results, crucial for clinical decision-making, especially when compared to more complex models like Artificial Neural Networks (ANN). This research offers a reliable and accurate tool for early breast cancer detection and prognosis, contributing to ongoing efforts to enhance patient outcomes through the integration of hybrid machine learning models in medical diagnostics.