Method For Detection Of Breast Cancer Research Articles

Factors associated with breast cancer detection method in California women: an analysis of California Health Interview Survey data.

Breast cancer mortality has significantly declined in the U.S. due in part to effective clinical screening methods. However, previous studies have found many women first detect their breast cancers through means other than their providers. Given that detection method has been shown to be an important prognostic factor, we examined the association between breast cancer detection method and various demographic and health-related factors in a representative sample of female breast cancer patients aged 40 + in California. We analyzed data from the 2009 and 2011-2012 California Health Interview Survey. Weighted percentages of breast cancer patients by detection method were calculated. A multivariable logistic regression model was used to quantify each factor's association with the likelihood of having a patient-detected versus clinically detected breast cancer using odds ratios (ORs) and 95% confidence intervals (CIs). 36% of female breast cancer patients first detected their cancer themselves. Compared to U.S.-born women, those who had spent 40% or less of their lifetime in the U.S. were more than twice as likely to report their breast cancer being patient-detected versus clinically detected (OR = 2.27, 95% CI 1.06-4.86, p = 0.035). Rural women were also more likely to report a patient-detected breast cancer than urban women (OR = 1.51, 95% CI 1.01-2.11, p = 0.044). Recent immigrants and those residing in rural areas were more likely to report self-detecting their breast cancer. Given the persistent disparities with regard to breast cancer mortality, particularly among underserved communities, strategies addressing barriers to screening uptake in these populations may be warranted.

Design and Development of a Quad mode Circular Millimeter-Wave Patch Antenna for Breast Tumour Size Detection and Localization

In this study, we present the development of an antenna based on millimeter-wave specifically designed for performance in breast tumour detection and localization. A quad – mode circular microstrip antenna is engineered to function at millimeter-wave frequencies of 30 GHz and 40 GHz, focusing on accurate tumor size & location. At 30 GHz and 40 GHz, facilitating effective penetration through breast tissue and enabling precise tumor localization. The differential dielectric characteristics of malignant and normal breast tissues provide the basis for the detection of breast cancers using mm-wave imaging. Using quarter mode circular patch antennas, this method sends and receives pulses that travel through the breast at various points. The tumor's location and size are then determined by receiving the waves from the breast. Due to their strong scattering properties, malignant tumors can be identified by their transfer characteristics in the reflected signals they receive. The study also looks at how the tumor's size and location in relation to the antenna affect detection efficiency. These results suggest that mm wave planar antennas are useful for locating and detecting tiny breast cancers, offering a viable method for breast cancer detection.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset—a revised variant of the Digital Database for Screening Mammography (DDSM)—offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

MR_NET: A Method for Breast Cancer Detection and Localization from Histological Images Through Explainable Convolutional Neural Networks.

Breast cancer is the most prevalent cancer among women globally, making early and accurate detection essential for effective treatment and improved survival rates. This paper presents a method designed to detect and localize breast cancer using deep learning, specifically convolutional neural networks. The approach classifies histological images of breast tissue as either tumor-positive or tumor-negative. We utilize several deep learning models, including a custom-built CNN, EfficientNet, ResNet50, VGG-16, VGG-19, and MobileNet. Fine-tuning was also applied to VGG-16, VGG-19, and MobileNet to enhance performance. Additionally, we introduce a novel deep learning model called MR_Net, aimed at providing a more accurate network for breast cancer detection and localization, potentially assisting clinicians in making informed decisions. This model could also accelerate the diagnostic process, enabling early detection of the disease. Furthermore, we propose a method for explainable predictions by generating heatmaps that highlight the regions within tissue images that the model focuses on when predicting a label, revealing the detection of benign, atypical, and malignant tumors. We evaluate both the quantitative and qualitative performance of MR_Net and the other models, also presenting explainable results that allow visualization of the tissue areas identified by the model as relevant to the presence of breast cancer.

A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.

Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness and non-ionizing radiation, has emerged as a promising tool for early breast cancer diagnosis. This article presents a hybrid model approach for breast cancer detection using thermography images, designed to process and classify these images into healthy or cancerous categories, thus supporting disease diagnosis. Multiple pre-trained convolutional neural networks are employed for image feature extraction, and feature filter methods are proposed for feature selection, with diverse classifiers utilized for image classification. Evaluating the DRM-IR test set revealed that the combination of ResNet34, Chi-square ( ) filter, and SVM classifier demonstrated superior performance, achieving the highest accuracy at . Furthermore, the highest accuracy improvement obtained was when using the SVM classifier and Chi-square filter compared to regular convolutional neural networks. The results confirmed that the proposed method, with its high accuracy and lightweight model, outperforms state-of-the-art breast cancer detection from thermography image methods, making it a good choice for computer-aided diagnosis.

Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.

Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetry and complexity of mammography images make segmentation difficult. The objective is to optimize the precision and effectiveness of medical imaging. The study introduces a hybrid strategy combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA), resulting in improved computational efficiency and performance. The implementation of Shape-guided segmentation (SGS) during the initialization phase, coupled with the elimination of convolutional layers, enables the model to effectively reduce computation time. The research proposes a novel loss function that combines segmentation losses from both components for effective training. The robust technique provided aims to improve the accuracy and consistency of breast tumor segmentation, leading to significant improvements in medical imaging and breast cancer detection and treatment. This study enhances breast cancer segmentation in medical imaging using CAD systems. Combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA) is a hybrid approach that improves performance and computational efficiency by dealing with complex data and not having enough training data. The approach also reduces computing time and improves training efficiency. The study aims to improve breast cancer detection and treatment methods in medical imaging technology.

Development of Statistically Modelled Feature Selection Method for Microwave Breast Cancer Detection

Development of Statistically Modelled Feature Selection Method for Microwave Breast Cancer Detection

Integration of feature enhancement technique in Google inception network for breast cancer detection and classification

Breast cancer is a major public health concern, and early detection and classification are essential for improving patient outcomes. However, breast tumors can be difficult to distinguish from benign tumors, leading to high false positive rates in screening. The reason is that both benign and malignant tumors have no consistent shape, are found at the same position, have variable sizes, and have high correlations. The ambiguity of the correlation challenges the computer-aided system, and the inconsistency of morphology challenges an expert in identifying and classifying what is positive and what is negative. Due to this, most of the time, breast cancer screen is prone to false positive rates. This research paper presents the introduction of a feature enhancement method into the Google inception network for breast cancer detection and classification. The proposed model preserves both local and global information, which is important for addressing the variability of breast tumor morphology and their complex correlations. A locally preserving projection transformation function is introduced to retain local information that might be lost in the intermediate output of the inception model. Additionally, transfer learning is used to improve the performance of the proposed model on limited datasets. The proposed model is evaluated on a dataset of ultrasound images and achieves an accuracy of 99.81%, recall of 96.48%, and sensitivity of 93.0%. These results demonstrate the effectiveness of the proposed method for breast cancer detection and classification.

Hybrid machine learning-based breast cancer segmentation framework using ultrasound images with optimal weighted features.

One of the most dangerous conditions in clinical practice is breast cancer because it affects the entire life of women in recent days. Nevertheless, the existing techniques for diagnosing breast cancer are complicated, expensive, and inaccurate. Many trans-disciplinary and computerized systems are recently created to prevent human errors in both quantification and diagnosis. Ultrasonography is a crucial imaging technique for cancer detection. Therefore, it is essential to develop a system that enables the healthcare sector to rapidly and effectively detect breast cancer. Due to its benefits in predicting crucial feature identification from complicated breast cancer datasets, machine learning is widely employed in the categorization of breast cancer patterns. The performance of machine learning models is limited by the absence of a successful feature enhancement strategy. There are a few issues that need to be handled with the traditional breast cancer detection method. Thus, a novel breast cancer detection model is designed based on machine learning approaches and employing ultrasonic images. At first, ultrasound images utilized for the analysis is acquired from the benchmark resources and offered as the input to preprocessing phase. The images are preprocessed by utilizing a filtering and contrast enhancement approach and attained the preprocessed image. Then, the preprocessed images are subjected to the segmentation phase. In this phase, segmentation is performed by employing Fuzzy C-Means, active counter, and watershed algorithm and also attained the segmented images. Later, the segmented images are provided to the pixel selection phase. Here, the pixels are selected by the developed hybrid model Conglomerated Aphid with Galactic Swarm Optimization (CAGSO) to attain the final segmented pixels. Then, the selected segmented pixel is fed in to feature extraction phase for attaining the shape features and the textual features. Further, the acquired features are offered to the optimal weighted feature selection phase, and also their weights are tuned tune by the developed CAGSO. Finally, the optimal weighted features are offered to the breast cancer detection phase. Finally, the developed breast cancer detection model secured an enhanced performance rate than the classical approaches throughout the experimental analysis.

New Frontiers in Breast Cancer Imaging: The Rise of AI.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

Study on breast cancer image detection and classification based on residual connected convolutional neural network (CNN)

This paper presents a method for breast cancer image detection and classification based on convolutional neural networks. While traditional breast cancer detection methods carry the risk of subjectivity and misclassification, machine learning can improve the accuracy and efficiency of detection by training algorithms to automatically identify breast cancer lesions in patients. The dataset of this paper includes 7909 microscopic images of breast tumor tissues with different magnifications collected from 82 patients, which are divided into two categories: benign and malignant tumors. In this paper, residual connection is designed for convolutional neural networks, which is a cross-layer connection method that can effectively solve the gradient vanishing and gradient explosion problems in deep neural networks, and at the same time, improve the generalization ability and training speed of the model. In this paper, the dataset is divided into training set, validation set and test set according to the ratio of 6:2:2. Through training and validation, the prediction accuracy of the obtained model on the test set reaches 85.5%, which achieves good prediction results in breast cancer image detection and classification. Eventually, the model's loss converged at 0.53 and the AUC was 0.832. The research results in this paper are of great significance for the early diagnosis and treatment of breast cancer. Automatic identification and classification of breast cancer images by machine learning algorithms can reduce the subjective judgment of doctors, improve the accuracy and efficiency of detection, and provide important support for the early diagnosis and treatment of breast cancer. Meanwhile, the research method and results of this paper also provide reference and reference for image detection and classification of other malignant tumors.

Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.

Prediction and analysis of breast cancer based on transfer learning from DDSM mammography images

Breast cancer is a cancer mostly found in women, causing a high morality rate. It appears as a lump in the breast initially, and as cancer cells spread, the other organs are endangered. Research shows that the earlier the breast cancer is detected, the higher rate of recovering will be. The main methods of breast cancer detection are mainly using various physical methods to probe the internal condition of the patients breast. Breast X-ray imaging, also known as mammography, is the most widely used. It uses low-dose X-ray technology to observe the breast tissue and help doctors figure out the exact condition of the patient. With the advancements in deep learning and image recognition, the analysis of mammograms can be previously done by the computer, easing the pressure on doctors. Digital Database for Screening Mammography (DDSM) dataset is a mammography dataset specifically designed for the development of mammography detection models. It contains huge amounts of mammograms and provides the labels corresponding to them. In this study, the performances of normal deep learning model and transfer learning model on the DDSM dataset are compared. Pretrained neural network models are employed and fine-tuned on the DDSM dataset to adapt to the features of mammographic images. Experimental results demonstrate significant improvements through transfer learning, outperforming traditional methods with Convolutional Neural Network (CNN). Finally, the models are evaluated in multiple aspects, conclusions and prospects are analysed based on the results.

Method of primary breast cancer detection and the disease-free interval, adjusting for lead time.

Little is known about the impact of screen-detected breast cancer compared with clinically detected breast cancer on the disease-free interval (ie, free from locoregional recurrences, distant metastasis, contralateral breast cancer). Moreover, it is thought that most studies overestimate the beneficial effect of screening, as they do not adjust for lead time. We investigated the association between method of breast cancer detection and disease-free interval, taking lead time into account. Women aged 50-76 years, diagnosed with breast cancer between 2005 and 2008 were selected from the Netherlands Cancer Registry. Women diagnosed in 2005 were divided into screen-detected and clinically detected cancer and had a follow-up of 10 years (2005 cohort). Women diagnosed in 2006-2008 were divided into screen-detected, interval, and nonscreen-related cancer and had a follow-up of 5 years (2006-2008 cohort). A previously published method was used to adjust for lead time. Analyses were repeated correcting for confounding variables instead of lead time. The 2005 cohort included 6215 women. Women with screen-detected cancer had an improved disease-free interval compared with women with clinically detected cancer (hazard ratio [HR] = 0.77, 95% confidence interval [CI] = 0.68 to 0.87). The 2006-2008 cohort included 15 176 women. Women with screen-detected or interval cancer had an improved disease-free interval compared with women with nonscreen-related cancer (HR = 0.76, 95% CI = 0.66 to 0.88; HR = 0.88, 95% CI = 0.78 to 0.99, respectively). Correcting for confounders instead of lead time did not change associations. Women with screen-detected cancer had an improved disease-free interval compared with women with a nonscreen-related or clinically detected cancer, after correction for lead time.

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

A Review on Computational Methods for Breast Cancer Detection in Ultrasound Images Using Multi-Image Modalities

Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability. In recent years, deep learning-based ML approaches developed to improve the diagnostic capabilities of CAD systems. This study reviews image enhancement, ML and DL methods for breast cancer detection and diagnosis using mammogram images and provides an overview of these methods. The analysis of different ways of ML and DL shows that the usages of traditional ML approaches are limited. However, DL techniques have an excellent future for implementing medical image analysis and improving the ability to exist CAD systems. Despite the significant advancements in deep learning methods for analyzing medical images to detect breast cancer, challenges still exist regarding data quality, computational cost, and prediction accuracy.

Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.

Optimizing Drug Delivery Systems for Enhanced Therapeutic Efficacy in Cancer Treatment: A Multidisciplinary Approach

This research aims to develop an early detection method for breast cancer through the analysis of specific biomarkers. The method leverages advanced drug delivery technology to enhance the efficacy of cancer therapy. Employing a multidisciplinary approach, this study integrates medical and pharmaceutical sciences to achieve optimal results. Specific biomarkers utilized in this research will be identified to improve the sensitivity and specificity of cancer detection. The findings of this research are anticipated to make a significant contribution to enhancing early breast cancer detection capabilities and optimizing therapy utilization in patients.

An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images

Breast cancer is the second major reason of death among women around the world. Early and accurate breast cancer detection is important for proper treatment planning to save a life. In this paper, a deep learning-based ensemble classifier is proposed for the detection of breast cancer. The primary contributions are: (1) an efficient deep learning-based breast cancer detection method that can exhibit admirable performance with a small dataset; (2) the integration of three efficient transfer learning models (AlexNet, ResNet, and MobileNetV2), which lead to more accurate results; (3) the use of residual learning, depthwise separable convolution, and inverted residual bottleneck structure to make the system faster, as well as skip connection to make optimization easier and lastly, employing Laplacian of Gaussian (LoG) and modified high-boosting to improve performance. The experimental results convey that the suggested scheme gives superior classification performance by achieving an accuracy of 99.17% to detect abnormality and 97.75% to detect malignancy on the mini-DDSM dataset. Similarly, on the ultrasound dataset (BUSI), it provides accuracies of 96.92% and 94.62% to detect abnormality and malignancy, respectively. It also gives the best performance in another ultrasound dataset, BUS2, with 97.50% accuracy. Therefore, because of its versatility and reliability, the proposed model can be used for breast cancer detection in multimodal datasets.

COVID-19 related change in breast cancer diagnosis, stage, treatment, and case volume: 2019–2021

PurposeEvaluate the COVID-19 pandemic impact on breast cancer detection method, stage and treatment before, during and after health care restrictions.MethodsIn a retrospective tertiary cancer care center cohort, first primary breast cancer (BC) patients, years 2019–2021, were reviewed (n = 1787). Chi-square statistical comparisons of detection method (patient (PtD)/mammography (MamD), Stage (0-IV) and treatment by pre-pandemic time 1: 2019 + Q1 2020; peak-pandemic time 2: Q2-Q4 2020; pandemic time 3: Q1-Q4 2021 (Q = quarter) periods and logistic regression for odds ratios were used.ResultsBC case volume decreased 22% in 2020 (N = 533) (p = .001). MamD declined from 64% pre-pandemic to 58% peak-pandemic, and increased to 71% in 2021 (p < .001). PtD increased from 30 to 36% peak-pandemic and declined to 25% in 2021 (p < .001). Diagnosis of Stage 0/I BC declined peak-pandemic when screening mammography was curtailed due to lock-down mandates but rebounded above pre-pandemic levels in 2021. In adjusted regression, peak-pandemic stage 0/I BC diagnosis decreased 24% (OR = 0.76, 95% CI: 0.60, 0.96, p = .021) and increased 34% in 2021 (OR = 1.34, 95% CI: 1.06, 1.70, p = .014). Peak-pandemic neoadjuvant therapy increased from 33 to 38% (p < .001), primarily for surgical delay cases.ConclusionsThe COVID-19 pandemic restricted health-care access, reduced mammography screening and created surgical delays. During the peak-pandemic time, due to restricted or no access to mammography screening, we observed a decrease in stage 0/I BC by number and proportion. Continued low case numbers represent a need to re-establish screening behavior and staffing.