Detection and Classification of Breast Cancer Using Different Machine Learning Classifier

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

The most severe disease nowadays is breast cancer. The abnormal development of breast cells is what causes breast cancer. The breast ducts may include this type of malignancy. Women over 40 are the ones who get diagnosed with breast cancer most frequently. Women over 50 have the highest incidence (about 77% of invasive cases). A clear understanding of breast cancer and its kinds, as well as the creation of prophylactic measures, have made significant strides. Many genes have been linked to breast cancer. Malignant and benign cells both fall within this category. Cancerous malignant conditions exist. Non-cancerous benign cancer. Malignant cells affect other cells of the body as well as the tissues continue to divide uncontrollably. While benign tumors are significantly less dangerous than malignant ones since they do not spread to other parts of the body. Treatment is not necessary for these cancers. Malignant tumors can be treated with chemotherapy, radiation therapy, and immunotherapy, among other forms of treatment. The algorithms used to develop this prediction system are machine learning-based. Machine learning became a research focus and was found to be an effective method. Recently, many scholars have become interested in the developing technique of machine learning. Scikit-learn is used to create a model that categorizes breast cancer as either malignant or benign. A type of library that is included with the collab platform is called Scikit-learn. Mammograms have historically been the primary method of finding breast cancer. Any calcium deposits or calcifications in the breasts might be seen on a mammogram. Considering how pricey it is, few people can afford it. It will be great if an early diagnosis occurs so that treatment can get underway right away. Many women's lives could be saved by this. This dataset analysis will benefit from the use of machine learning algorithms. Six machine learning (ML) methods are compared in this paper: Naive Bayes (NB), Random Forest (RT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). These methods will be applied to accurately forecast outcomes. Due to early detection, the 5-year survival rate for breast cancer patients is above 82 %. The eventual goal of this project is to assist clinicians in analyzing enormous databases of available cancer data and discovering patterns using patient data and that cancer data. This endeavor to a modest stride in the protracted battle against breast cancer.

Machine learning-based classification of breast cancer and its detection is possible without toxic therapy by a well-trained model. The machine learning model detects features and patterns form the data sets that used when training model which is useful for detecting tumor and classify whether it is a benign or malignant and this process simplifies the cancer detection and gives results accurately at a faster rate when compared to the other traditional methods like Magnetic resonance imaging (MRI), Coronary artery disease (CAD), Modalities using ultrasound, etc. Here I am proposing a new technique through which breast cancer can be easily detected by a proper training model with the help of few classifying algorithms in this research a good set of data is used for training classifier machine algorithms in Microsoft azure by comparing all those five algorithms accuracy and working these are the five algorithm models are 2-class Support vector machine, 2-class Neural Networks, 2-class Boosting Tree, 2- Class Logistic Regression, 2-Class Bayes Point and acquired better results which can lead and helpful for detecting cancer in future by using machine learning and deep learning techniques.

Detection of breast cancer remains an important global health issue, particularly in patients with dense breast tissue. Despite wide use of machine learning for breast cancer diagnosis, many existing studies rely on single-model evaluations and lack rigorous statistical validation, while transformer-based models remain insufficiently explored for structured tabular biomedical data. Using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, this study presents a comparative analysis of machine learning, deep learning, and transformer-based models for binary classification of breast masses as benign or malignant. A robust preprocessing and feature engineering pipeline was applied, and Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and BERT-based models were evaluated using a stratified 66–34% train–test split and stratified 5-fold cross-validation. Performance was assessed using standard classification metrics, Jaccard Index, AUC-ROC, false negative rate, and statistical significance testing (paired t-tests and Bayesian analysis). Three-layer RNN and LSTM models achieved the highest accuracy of 98.25%, showing statistically significant improvement over SVM (p = 0.042), while the optimized SVM demonstrated strong performance with superior computational efficiency. BERT-based models yielded lower accuracy (92.98%), reflecting domain mismatch between language-pretrained transformers and numerical tabular data. These findings indicate that deeper recurrent architectures provide superior diagnostic performance, while SVM remains a practical and efficient alternative for clinical deployment.

This study deals with the linear, polynomial, and radial basis function (RBF) kernel-based support vector machines (SVM) applied to the classification of breast cancer. It checks the efficiency of different kernel-based techniques for distinguishing benign and malignant tumors against medical datasets. This analysis is conducted on the public Wisconsin Breast Cancer Dataset borrowed from the UCI Machine Learning Repository, which consists of 569 cases, 357 benign and 212 malignant. It aims to develop the assessment necessary for each Kernel based on its accuracy, precision, recall, computational efficiency, F1 score, and area under the curve (AUC). The outcome reveals that, in general, the polynomial Kernel will provide top-ranking results across the various features, thereby enhancing accuracy (96.84%), specificity (97.76%), and AUC (0.9966) compared to the linear and RBF kernels for use in disease classifications of cancers. These findings indicate that the polynomial kernel adeptly captures complex, non-linear data relationships, making it a strong candidate for developing more accurate SVM-based breast cancer detection models.

Rationale and ObjectivesThe human epidermal growth factor receptor 2 (HER2) gene status is crucial for determining treatment efficacy. This study assessed preoperative HER2 classification in breast cancer using machine learning based on clinicopathological and MRI characteristics.Materials and MethodsThis retrospective study involved 1,015 patients (1,030 lesions) across two centers. Patients were divided into training, internal validation, and external validation sets. Nomograms were developed using clinicopathological and MRI features. Predictive models were constructed using decision trees (DT), support vector machines (SVM), k-nearest neighbors (k-NN), artificial neural networks (ANN), and multivariable logistic regression (LR). Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and calibration curves. Model interpretability was achieved by developing nomograms and employing SHAP (SHapley Additive exPlanations) analysis.ResultsKey variables for distinguishing HER2-positive from HER2-negative cases included regional N category, estrogen receptor, PR (progesterone receptor) status, Ki-67 status, lesion number, distribution quadrant, and accompanying signs. The SVM model achieved the highest AUC of 0.86 (95% confidence interval (CI): 0.81–0.90) in the training set, while the ANN model had an AUC of 0.77 (95% CI: 0.67–0.86) in the internal validation set. In the external validation set, the LR model achieved the highest AUC of 0.66 (95% CI: 0.56–0.76), although the overall performance was modest. For HER2-low versus HER2-zero differentiation, Ki-67 status, lesion number, distribution quadrant, mass shape, early enhancement rate, and ADC (apparent diffusion coefficient) were significant. The SVM model attained the highest AUC of 0.87 (95% CI: 0.83–0.91) in the training set, while the LR model demonstrated superior generalizability, yielding the highest AUCs in both the internal and external validation sets (internal: 0.67, 95% CI: 0.58–0.76; external: 0.74, 95% CI: 0.65–0.83). Radiologists benefited from the nomogram for improved diagnostic accuracy, especially junior radiologists. SHAP analysis revealed that PR status was paramount for HER2-positive classification, whereas mass shape and ADC values were dominant for identifying HER2-low status.ConclusionIntegrating machine learning with clinicopathological and MRI characteristics improves the accuracy of HER2 status classification in breast cancer and enhances diagnostic capabilities for radiologists in clinical practice.

The Promise Fund and Hologic Inc. have partnered to expand access to AI-supported breast cancer screening exams in a medically underserved population. As part of this initiative, patient focus groups and patient navigator interviews were conducted to explore patient experiences and perceptions related to breast health, mammography, and artificial intelligence (AI) in healthcare. These interviews were held, virtually, between February and March 2024, and focus groups were held, in-person, on April 9-10, 2024, with participants at FoundCare and the Community Health Center. FoundCare, a federally qualified health center (FQHC), and Community Health Center, a free clinic, both collaborate with the Promise Fund in promoting access to women’s breast and cervical health screenings, diagnostic follow up and cancer care services. The in-person sessions, attended by twenty patients, and the virtual interviews, attended by six patient navigators, were designed to represent diverse linguistic, racial, and cultural backgrounds, including multiple Spanish-speaking communities from Mexico, Dominican Republic, Guatemala, Chile, and Venezuela. All patient navigators were adult women from various free clinics and FQHCs. Patient participants were adult females, all at or below 200% of the Federal Poverty Guidelines. Key themes from the discussions included: 1. Health Information Sources: Patients primarily relied on the internet, community health centers, medical visits, family, and cultural community communications for health information. Some patients expressed difficulty in keeping up with health information and emphasized the importance of direct medical consultations with providers and preventive care. Patient navigators were a critical source of information once the patient could access and be connected to that resource. Clinical health and more upstream social services, such as transportation to health appointments and family food security, were also critical needs that patient navigators were key in facilitating. 2. Mammogram Experiences: Experiences with mammograms varied, with some patients reporting pain and discomfort, particularly those with implants. Regular screenings, family history of breast cancer, and personal vigilance were common motivators for mammograms. Financial barriers, insurance issues, and the need for patient advocacy were frequently highlighted. 3. AI in Healthcare: Patients had mixed levels of awareness and understanding of AI. Many associated AI with advanced diagnostics and potential improvements in early disease detection and surgical precision. Concerns included the potential loss of personal interactions with healthcare providers, privacy issues, and the fear of job displacement. However, patients had key opinions on AI that were more favorable than anticipated. There was optimism about AI’s ability to enhance diagnostic accuracy, patient experience, provider trustworthiness, and treatment outcomes. Additionally, both patients and patient navigators preferred that physicians and AI work together. Patient navigators were even more familiar with AI. Given the critical role patient navigators hold, their knowledge may play an integral role in improving patient education and access to new technologies in the future. 4. Whole Health Experience: Patients expressed a desire for a more comfortable and supportive mammography process, greater access to care, enhanced trust between patients and providers, and comprehensive information sharing across healthcare providers. Education on breast cancer, early detection, and AI in healthcare was identified as a critical need. Conclusion: The findings underscore the importance of trustworthy, patient-centered approaches in healthcare, particularly in the integration of AI technologies. Enhancing patient education and provider transparency, improving access to preventive services, and maintaining the human element in healthcare are essential for optimizing patient outcomes and satisfaction. Citation Format: Nancy Brinker. Patient Experience and Perceptions Related to Breast Health, Mammography and Artificial Intelligence in Healthcare [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-05-11.

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 (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morphological features were extracted, along with features from deep learning architecture VGG16. Then, we employed a hybrid optimization model, combining the Sparrow Search Algorithm (SSA) and Red Deer Algorithm (RDA), called Hybrid Red Deer with Sparrow optimization (HRDSO), to select the most informative subset of features. For detecting BC, we proposed a new DenseXtNet architecture by combining DenseNet and optimized ResNeXt, which is optimized using the hybrid optimization model HRDSO. The proposed model was evaluated using various performance metrics and compared with existing methods, demonstrating that its accuracy is 97.58% in BC detection. MATLAB was utilized for implementation and evaluation purposes.

Breast Cancer is the hazardous infection among young ladies and most significant reason for developing destruction rate. Physically anticipation of this affliction takes longer hours and structures are to be had in significantly less number, there’s motivation to expand a mechanized forecast machine for early expectation and visualization of malignancy. The sort of considerate and threatening tumor are accomplished the use of type methodologies of device becoming acquainted with wherein the device is found from the past records and predicts the sorts of late sources of info. This paper is an overall report on the execution of models utilizing Logistic Regression, Support Vector Machine (SVM) and Gray wolf algorithm(GWO). The outcomes are assessed with the exactness, accuracy, affectability, explicitness and False Positive Rate boundaries for every calculation and are thought about. These procedures are coded in Java and executed in MATLAB, the Image handling Environment. Our investigations have demonstrated that Gray wolf is the best for prescient examination with an exactness of 92.7%.We surmise from our test results that SVM is the appropriate calculation for expectation and in general Gray wolf has performed well close to SVM.

Breast cancer is one among the most frequent reasons of women's death worldwide. Nowadays, healthcare informatics is mainly focussing on the classification of breast cancer images, due to the lethal nature of this cancer. There are chances of inter- and intra-observer variability that may lead to misdiagnosis in the detection of cancer. This study proposed an automatic breast cancer classification system that uses support vector machine (SVM) classifier based on integrated features (texture, geometrical, and color). The University of California Santa Barbara (UCSB) dataset and BreakHis dataset, which are available in public domain, were used. A classification comparison module which involves SVM, k-nearest neighbor (k-NN), random forest (RF), and artificial neural network (ANN) was also proposed to determine the classifier that best suits for the application of breast cancer detection from histopathology images. The performance of these classifiers was analyzed against metrics like accuracy, specificity, sensitivity, balanced accuracy, and F-score. Results showed that among the classifiers, the SVM classifier performed better with a test accuracy of approximately 90% on both the datasets. Additionally, the significance of the proposed integrated SVM model was statistically analyzed against other classifier models.

The proposed research work aims to develop a method to predict and classify breast cancer (BC) at an early stage. In this research, three models are developed, and their performance is compared against each other. The first model was built using one of the machine learning algorithms called support vector machine (SVM), the second model was built using a deep learning algorithm called convolutional neural networks (CNNs), and the third model combines CNNs with a transfer learning technique for delivering better results. The data set is provided by the BC Histopathological Image Classification (BreakHis). All models are trained on the training set with two main categories: benign tumor and malignant tumor. The malignant tumor category is divided into subsets of invasive carcinoma tumors and in situ carcinoma tumors. Furthermore, invasive carcinoma tumors are classified into grade 1, grade 2, or grade 3, where grade 3 is the highest and is more aggressive. The results show that the accuracies of biopsy image classification using SVM are 92%, the accuracy of CNN is 94%, and the accuracy of CNN using the transfer learning technique is 97%. The results of this research will be beneficial in the early diagnosis of BC and help doctors in making better decisions and medical interventions.

Breast cancer remains a major global health concern, particularly among women, where early detection is crucial for effective treatment and increased survival rates. Manual diagnosis through histopathological examination is often time-consuming, prone to human error, and requires expert interpretation. In response to this challenge, we developed a deep learning-based system to automate the detection and classification of breast cancer using microscopic biopsy images, aimed at enhancing diagnostic accuracy and speed in clinical workflows. Our project employs convolutional neural networks (CNNs) to classify histopathological images into three distinct categories: normal, benign, and malignant. We utilized a publicly available dataset containing high-resolution microscopic images and implemented preprocessing techniques to improve image quality, normalize input dimensions, and optimize feature extraction. The model was developed and trained using TensorFlow with GPU support, allowing for efficient computation and faster training cycles. Key Words: Breast cancer, Cancer detection, Cancer classification, Histopathological images, microscopic images, Digital pathology, medical imaging

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.

Rationale and ObjectivesBreast cancer molecular subtypes significantly influence treatment outcomes and prognoses, necessitating precise differentiation to tailor individualized therapies. This study leverages multimodal ultrasound imaging combined with machine learning to preoperatively classify luminal and non-luminal subtypes, aiming to enhance diagnostic accuracy and clinical decision-making.MethodsThis retrospective study included 247 patients with breast cancer, with 192 meeting the inclusion criteria. Patients were randomly divided into a training set (134 cases) and a validation set (58 cases) in a 7:3 ratio. Image segmentation was conducted using 3D Slicer software, adhering to IBSI-standardized radiomics feature extraction. We constructed four model configurations—monomodal, dual-modal, trimodal, and four-modal—through optimized feature selection. These included monomodal datasets comprising 2D ultrasound (US) images, dual-modal datasets integrating 2D US with color Doppler flow imaging (CDFI) (US+CDFI), trimodal datasets incorporating strain elastography (SE) alongside 2D US and CDFI (US+CDFI+SE), and four-modal datasets combining all modalities, including ABVS coronal imaging (US+CDFI+SE+ABVS). Machine learning classifiers such as logistic regression (LR), support vector machines (SVM), AdaBoost (adaptive boosting), random forests(RF), linear discriminant analysis(LDA), and ridge regression were utilized.ResultsThe four-modal model achieved the highest performance (AUC: 0.947, 95% CI: 0.884-0.986), significantly outperforming the monomodal model (AUC 0.758, ΔAUC +0.189). Multimodal integration progressively enhanced performance: trimodal models surpassed dual-modal and monomodal approaches (AUC 0.865 vs 0.741 and 0.758), and the four-modal framework showed marked improvements in sensitivity (88.4% vs 71.1% for monomodal), specificity (92.7% vs 70.1%), and F1 scores (0.905).ConclusionThis study establishes a multimodal machine learning model integrating advanced ultrasound imaging techniques to preoperatively distinguish luminal from non-luminal breast cancers. The model demonstrates significant potential to improve diagnostic accuracy and generalization, representing a notable advancement in non-invasive breast cancer diagnostics.