Approach For Breast Cancer Detection Research Articles

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography and biopsy are laborious, invasive, and inefficient; confocal microscopy offers many benefits over these approaches. However, confocal microscopy enables the exact differentiation of malignant cells, the expeditious examination of extensive tissue sections, and the optical sectioning of tissue samples into tiny slices. The primary goal should be to prevent cancer altogether, although detecting it early can help achieve that objective. This research presents a novel Breast Histopathology Convolutional Neural Network (BHCNN) for feature extraction and recursive feature elimination method for selecting the most significant features. The proposed approach utilizes full slide images to identify tissue in regions affected by invasive ductal carcinoma. In addition, a transfer learning approach is employed to enhance the performance and accuracy of the models in detecting breast cancer, while also reducing computation time by modifying the final layer of the proposed model. The results showed that the BHCNN model outperformed other models in terms of accuracy, achieving a testing accuracy of 98.42% and a training accuracy of 99.94%. The confusion matrix results show that the IDC positive (+) class achieved 97.44% accuracy and 2.56% inaccurate results, while the IDC negative (-) class achieved 98.73% accuracy and 1.27% inaccurate results. Furthermore, the model achieved less than 0.05 validation loss. RESEARCH HIGHLIGHTS: The objective is to develop an innovative framework using ultra-fast fluorescence confocal microscopy, particularly for the challenging problem of breast cancer diagnosis. This framework will extract essential features from microscopy and employ a gradient recurrent unit for detection. The proposed research offers significant potential in enhancing medical imaging through the provision of a reliable and resilient system for precise diagnosis of breast cancer, thereby propelling the progression of state-of-the-art medical technology. The most suitable feature was determined using BHRFE optimization techniques after retrieving the features by proposed model. Finally, the features chosen are integrated into a proposed methodology, which is then classified using a GRU deep model. The aforementioned research has significant potential to improve medical imaging by providing a complex and reliable system for precise evaluation of breast cancer, hence advancing the development of cutting-edge medical technology.

A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data

Abstract Early detection is the most effective defense against breast cancer. Mammography is a well-established X-ray based technique that is used for annual or biennial screening of women above age of 40. Since the dense breast tissue sometimes obscures the cancer in an x-ray image, about 10% of screened women are recalled and undergo additional adjunctive modalities, such as ultrasound, digital breast tomosynthesis or magnetic resonance imaging (MRI). These modalities have drawbacks such as additional radiation dosage, overdiagnosis and high cost. A new concurrent multi-spectral imaging approach is recently presented to eliminate the high recall rates by utilizing the breast surface temperature data with an inverse physics-informed neural network algorithm. The multi-spectral imaging does not use any harmful radiations, such as x-rays, is contact-less and does not require breast compression. It has been validated in 23 patients and offers a cost-effective solution to provide improved detection capability of cancerous cells. It is estimated to reduce the recall rates significantly from the current 10%, with a corresponding reduction in the biopsies. This adjunctive approach builds on the strength of mammography and offers a safe adjunct by relying on the higher metabolic rates of cancer cells.

A hybrid random forest and k-nearest neighbors approach for breast cancer detection

A hybrid random forest and k-nearest neighbors approach for breast cancer detection

Automatic approach for breast cancer detection based on deep belief network using histopathology images

Automatic approach for breast cancer detection based on deep belief network using histopathology images

A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes.

Federated Learning Approach for Breast Cancer Detection Based on DCNN

Federated Learning Approach for Breast Cancer Detection Based on DCNN

Transfer and Ensemble Approach for Breast Cancer Detection and Classification Using Deep Learning

Breast cancer is a serious disease that can cause significant health problems for women worldwide. It is crucial to detect and classify breast cancer early stage so that doctors can promptly treat it and aid patients in their recovery. Many investigators have used various deep learning (DL) strategies to detect and classify breast cancer. However, due to the complexity of the problem, relying on a single DL model may not suffice to achieve high accuracy. Therefore, this study suggests a transfer and ensemble deep model for breast cancer detection and classification. The suggested model involves using pre-trained models such as Sequential, Xception, DenseNet201, VGG16, and InceptionResNetV2. The top three models are selected to collaborate and deliver the most accurate results. The proposed DL model was tested on publicly available breast BUSI datasets, demonstrating its superiority over single DL models, achieving an accuracy of 87.9% on the BUSI dataset. Additionally, the model proved to be adapTABLE to different amounts of data, making it potentially valuable in hospitals and clinics.

Comment on "Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer".

This paper comments recent findings about Raman spectroscopy application for breast cancer detection published in the Photodiagnosis and Photodynamic Therapy journal by Li etal. Despite the high performance of the proposed approach for breast cancer detection by means of Raman spectroscopy analysis of serum, the proposed results may be ambiguous due to overestimation of classification models.

Microwave Imaging Approach for Breast Cancer Detection Using a Tapered Slot Antenna Loaded with Parasitic Components

In this paper, a wideband antenna is proposed for ultra-wideband microwave imaging applications. The antenna is comprised of a tapered slot ground, a rectangular slotted patch and four star-shaped parasitic components. The added slotted patch is shown to be effective in improving the bandwidth and gain. The proposed antenna system provides a realized gain of 6 dBi, an efficiency of around 80% on the radiation bandwidth, and a wide impedance bandwidth (S11 < -10 dB) of 6.3 GHz (from 3.8 to 10.1 GHz). This supports a true wideband operation. Furthermore, the fidelity factor for face-to-face (FtF) direction is 91.6%, and for side by side (SbS) is 91.2%. This proves the excellent directionality and less signal distortion of the designed antenna. These high figures establish the potential use of the proposed antenna for imaging. A heterogeneous breast phantom with dielectric characteristics identical to actual breast tissue with the presence of tumors was constructed for experimental validation. An antenna array of the proposed antenna element was situated over an artificial breast to collect reflected and transmitted waves for tumor characterization. Finally, an imaging algorithm was used to process the retrieved data to recreate the image in order to detect the undesirable tumor object inside the breast phantom.

A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection

A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection

Hybrid PSO feature selection-based association classification approach for breast cancer detection

Hybrid PSO feature selection-based association classification approach for breast cancer detection

Breast cancer classification on thermograms using deep CNN and transformers

ABSTRACT Breast thermography is a screening approach for breast cancer detection by measuring the breast skin temperature. Breast cancer is the most common cancer among women and can affect either women or men. Its early diagnosis and treatment reduce deaths and increase survival chances. The use of deep learning algorithms and techniques has made it easier to detect breast cancer in its early stages, but some challenges remain. In this work, we propose a breast cancer detection system. In the first step, TransUNet, a vision-based Transformer, is used to segment the breast region and separate it from the rest of the body. In the second step, four different models such as EfficientNet-B7, ResNet-50, VGG-16 and DenseNet-201 are used to classify the dataset into three types: healthy, sick, and unknown. The best accuracy achieved is 97.26%, sensitivity of 97.26% and specificity of each class healthy, sick, unknown is 100%, 96.94% and 99.72% respectively with the ResNet-50 model.

"Some They Need Male, Some They Need Female": A Gendered Approach for Breast Cancer Detection in Uganda.

IntroductionThere are several challenges associated with breast cancer detection in Uganda and other low-and-middle-income countries. One of the identified challenges is attributed to the health workers' gender, which facilitates gender disparities in access to breast cancer detection services. Although this challenge is well acknowledged in existing literature, there are hardly any studies on how it can be addressed. Therefore, drawing on an intersectionality lens, our study examined how to address gender disparities facilitated by health workers' gender in accessing breast cancer detection services in Uganda.Materials and MethodsWe collected qualitative data through semi-structured interviews with twenty participants comprising community health workers, primary health care practitioners, non-governmental organizations, district health team, and the Ministry of Health. For the data analysis, thematic analysis was conducted on NVivo using Braun and Clarke's non-linear 6-step process to identify the themes presented in the results section.ResultsFour themes emerged from the data analysis: understanding a woman's gender constructions; health workers' approachability; focus on professionalism, not sex; and change in organizational culture. These themes revealed participants' perceptions regarding how to address gender disparities relating to the role health workers' gender play in breast cancer detection. Through the intersectionality lens, our findings showed how gender intersects with other social stratifiers such as religious beliefs, familial control, health worker's approachability, and professionalism within the health workforce.ConclusionOur findings show that the solutions to address gender disparities in breast cancer detection are individually and socially constructed. As such, we recommend a gendered approach to understand and redress the underlying power relations perpetuating such constructions. We conclude that taking a gendered approach will ensure that breast cancer detection programs are context-appropriate, cognizant of the prevailing cultural norms, and do not restrict women's access to breast cancer detection services.

A Novel Approach for Breast Cancer Detection and Classification with Transfer Learning

Bosom malignant growth is a deadly infection that is liable for the passing of ladies everywhere. Bosom Malignant growth is recognized as the main worry for ladies deaths. There are different sorts of bosom disease. The proposed model talked about harmless and dangerous bosom disease. In PC supported analysis frameworks, the distinguishing proof and arrangement of bosom malignant growth utilizing histopathology and ultrasound pictures are basic advances. Deep learning (DL), machine learning (ML), and transfer learning (TL) strategies are utilized to address numerous clinical issues. The proposed approach is made to assist with the programmed recognizable proof and determination of bosom malignant growth. Our principal commitment is that the proposed model utilized the transfer learning strategy. The model utilized in this work is a tweaked CNN-AlexNet, which was prepared by the prerequisites of the datasets. This is likewise one of the commitments of this work.

Comprehensive profiles and diagnostic value of menopausal-specific gut microbiota in premenopausal breast cancer

In Western countries, breast cancer tends to occur in older postmenopausal women. However, in Asian countries, the proportion of younger premenopausal breast cancer patients is increasing. Increasing evidence suggests that the gut microbiota plays a critical role in breast cancer. However, studies on the gut microbiota in the context of breast cancer have mainly focused on postmenopausal breast cancer. Little is known about the gut microbiota in the context of premenopausal breast cancer. This study aimed to comprehensively explore the gut microbial profiles, diagnostic value, and functional pathways in premenopausal breast cancer patients. Here, we analyzed 267 breast cancer patients with different menopausal statuses and age-matched female controls. The α-diversity was significantly reduced in premenopausal breast cancer patients, and the β-diversity differed significantly between breast cancer patients and controls. By performing multiple analyses and classification, 14 microbial markers were identified in the different menopausal statuses of breast cancer. Bacteroides fragilis was specifically found in young women of premenopausal statuses and Klebsiella pneumoniae in older women of postmenopausal statuses. In addition, menopausal-specific microbial markers could exhibit excellent discriminatory ability in distinguishing breast cancer patients from controls. Finally, the functional pathways differed between breast cancer patients and controls. Our findings provide the first evidence that the gut microbiota in premenopausal breast cancer patients differs from that in postmenopausal breast cancer patients and shed light on menopausal-specific microbial markers for diagnosis and investigation, ultimately providing a noninvasive approach for breast cancer detection and a novel strategy for preventing premenopausal breast cancer.

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression

Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.

ASSESSMENT OF ASYMMETRY IN BILATERAL STATIC FRONTAL BREAST THERMOGRAMS USING DIFFERENCE IMAGE AND RADIOMIC FEATURES

Asymmetry analysis of bilateral thermogram images is an important preliminary approach for breast cancer detection. The purpose of this work is to develop an automated algorithm to detect and classify symmetric and asymmetric bilateral static frontal breast thermograms (N=63). The images are pre-processed using anisotropic diffusion filter for removal of noise. Further, segmentation of complete breast region is carried out using level set segmentation without re-initialization. The bifurcation point is computed from the intersection point of interior inframammary curves attained using polynomial curve fitting on the boundary pixels. The obtained breast region is sliced vertically along this bifurcation point to obtain right and left breast sections. Image subtraction is performed between right breast image and flipped left breast image to obtain the difference image. The obtained difference image is sharpened and 144 texture features such as first-order statistical, co- occurrence, run length and laws energy features are extracted and Absolute Difference (AD) between symmetric and asymmetric subjects for each feature is computed. The features for which the value of AD is greater than 0.1 is considered as substantial features. Twenty four substantial features are obtained and are given as an input to Least Square Support Vector Machine (LSSVM) to automate the classification. The results shows that the maximum segmentation overlap measure obtained is 98.3%. The classification accuracy obtained using LSSVM with Radial Basis Function (RBF) is 95.65% and sensitivity, specificity and Area Under the Curve (AUC) are 100%, 90.9% and 0.9545 respectively. Thus the proposed methodology appears to be effective in detecting asymmetric heat patterns and hence can be deployed in thermal screening systems.

BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm.

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

SVM &amp;Ga-clustering Based Feature Selection Approach for Breast Cancer Detection

Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.