Accuracy Of Breast Cancer Detection Research Articles

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

Comparative Analysis of Artificial Intelligence Algorithms for Breast Cancer Detection from Pathological Image in Burkina Faso.

Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep learning, enabling the development of algorithms for automatic processing of medical images from massive medical data. In Burkina Faso, early and accurate detection of breast cancer is a significant challenge due to limited resources and lack of specialized expertise. In this article, we examine the effectiveness of different artificial intelligence algorithms for breast cancer detection from pathological image.

A reliable breast cancer diagnosis approach using an optimized deep learning and conformal prediction

Accuracy of breast cancer detection using histopathological images is mainly subject to the expertise of physicians. For this reason, there are certain levels of uncertainty during the decision making of breast cancer diagnosis. To increase the certainty of physicians’ decisions, computer-aided medical systems can be used as additional knowledge for diagnosis from histopathological images. Early detection of breast cancer contributes greatly to the survival rate of the patients, which is essentially dependent on the analysis of breast images. In the literature review, few studies have been proposed for breast cancer classification based on histopathological images. However, the performance of current methods is still inefficient and they are incapable for extracting important features from the images due to ineffective hyper-parameters optimization. In this paper, a reliable deep learning approach for breast cancer diagnosis is proposed using a random search algorithm and DenseNet-121-based transfer-learning model. For the transfer learning technique, the pre-trained DenseNet-121 model is adopted due to its capability to strengthen the feature representation and reduce the number of trainable parameters. The use of random search optimization finds the best combinations of hyper-parameters values to improve the accuracy of the classification approach. Several experiments are conducted on a benchmark public dataset of histopathological images with different magnification factors: 40×, 100×, 200×, and 400×. The experiments showed that the proposed approach achieved 98.96 %, 97.62 %, 97.08 %, and 96.42 % of accuracy for 40×, 100×, 200×, and 400× test images, respectively, demonstrating its effectiveness compared to the state-of-the-art models and methods. The reliability of proposed approach is achieved by quantifying the uncertainty of model outcomes using conformal prediction method, guarantying user-chosen levels of confidence.

Inception ResNet v2 for Early Detection of Breast Cancer in Ultrasound Images

Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods. Previous research has shown that the use of the CNN (Convolutional Neural Network) algorithm in breast cancer detection still does not achieve high accuracy. This study aims to improve the accuracy of breast cancer detection using the Inception ResNet v2 transfer learning method and data augmentation. The data is divided into training, validation and testing data consisting of 3 classes, namely Benign, Malignant and Normal. The augmentation process includes rotation, zoom, and rescale. The model trained using CNN and Inception ResNet v2 showed good performance by producing the highest accuracy of 89.72% in the training data evaluation data and getting 90% accuracy in the prediction test stage with data testing. This study shows that the combination of data augmentation and the Inception ResNet v2 architecture can improve the accuracy of breast cancer detection in CNN models.

Breast cancer detection employing stacked ensemble model with convolutional features.

Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.

Site-Specific Cascade-Activatable Fluorogenic Nanomicelles Enable Precision and Accuracy Imaging of Pulmonary Metastatic Tumor.

The precise localization of metastatic tumors with subtle growth is crucial for timely intervention and improvement of tumor prognosis but remains a paramount challenging. To date, site-specific activation of fluorogenic probes for single-stimulus-based diagnosis typically targets an occult molecular event in a complex biosystem with limited specificity. Herein, we propose a highly specific site-specific cascade-activated strategy to enhance detection accuracy, aiming to achieve the accurate detection of breast cancer (BC) lung metastasis in a cascade manner. Specifically, cascade-activatable NIR fluorogenic nanomicelles HPNs were constructed using ultra-pH-sensitive (UPS) block copolymers as carriers and nitroreductase (NTR)-activated fluorogenic reporters. HPNs exhibit programmable cascade response characteristics by first instantaneous dissociating under in situ tumor acidity, facilitating deep tumor penetration followed by selective fluorescence activation through NTR-mediated enzymatic reaction resulting in high fluorescence ON/OFF contrast. Notably, this unique feature of HPNs enables high-precision diagnosis of orthotopic BC as well as its lung metastases with a remarkable signal-to-background ratio (SBR). This proposed site-specific cascade activation strategy will offer opportunities for a specific diagnosis with high signal fidelity of various insidious metastatic lesions in situ across different diseases.

Integration of Complex Mathematical Approaches and Statistical Methodologies in Machine Learning for Design of Robust Prediction Models

The disease known as breast cancer continues to be one of the most common and potentially fatal diseases that affect women all over the world. In order to provide effective therapy and more favorable outcomes for patients, early detection and correct diagnosis are absolutely necessary. This research explores the integration of operational research (OR) methodologies and advanced statistical methods within machine learning frameworks to enhance the prediction accuracy of breast cancer outcomes. By harnessing the power of nonlinear optimization and statistical techniques, including regression analysis and probability distribution models, we aim to refine the predictive capabilities of existing algorithms. The research employs a comprehensive dataset derived from clinical trials and patient records, analyzed through a series of machine learning models that incorporate elements of combinatorial optimization, decision analysis, and stochastic modeling. Key performance metrics, such as accuracy, sensitivity, and specificity, are evaluated against standard benchmarks to determine the efficacy of the integrated approaches. Preliminary results indicate that incorporating OR and statistical methods significantly improves model robustness and predictive accuracy. The study not only demonstrates the potential of applied mathematics in medical diagnostics but also provides a framework for future research in enhancing machine learning models for health outcomes prediction through mathematical innovations. This investigation contributes to the field of mathematical oncology by demonstrating how applied nonlinear analysis can bridge the gap between theoretical mathematical approaches and practical clinical applications, offering new pathways for early and more accurate detection of breast cancer.

Optimizing Breast Cancer Detection: Deep Transfer Learning Empowered by SVM Classifiers

Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold cross-validation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images. Keywords: Breast Cancer, Deep Learning, Feature Extraction, Inception-v3, SVM, Transfer Learning

Point-of-Care Detection of HER2 and CA 15-3 in Breast Cancer Patients: Dual-Channel Biosensor Implementation

Breast cancer remains a considerable health challenge, affecting numerous individuals annually. This research introduces an innovative method for detecting breast cancer utilizing dual-channel test strips capable of simultaneously assessing two key biomarkers—HER2 and CA 15-3. The test strip utilized in this study is not only cost-effective but also entirely non-invasive. The reusable device employs a printed circuit board with metal-oxide-semiconductor field-effect transistor amplification and Arduino-based control to convert voltage signals from test strips into digital readings efficiently. The device utilizes double-pulse measurement instead of direct current, effectively mitigating the screening effect. The detection limit for both biomarkers is exceptionally low at 10−15 g ml−1, surpassing commercial enzyme-linked immunoassay kits by four orders of magnitude. The sensor demonstrates remarkable sensitivity, with 78/dec for HER2 and 56/dec for CA 15-3. Human sample tests were conducted to validate the efficacy of the dual-channel strip, successfully distinguishing between healthy and cancerous groups. The results reveal significant p-values for both HER2 and CA 15-3 tests, underscoring the significance of this research. Note that this is a rapid testing process, completed in less than 2 s. These findings offer a promising avenue for swift and accurate breast cancer detection, furnishing crucial insights for early diagnosis and subsequent treatment.

The Combination of Feature Extraction and Classification by Bag of Visual Words to Detect Breast Cancer for Improved Accuracy

The main objective is to improve diagnostic accuracy of the breast cancer. The goal is to improve the accuracy of breast cancer detection by leveraging both the discriminative power of feature extraction and the robustness of classification using BoVW. By combining these techniques, the system aims to effectively distinguish between cancerous and non-cancerous tissues in medical images, thereby aiding in early diagnosis and treatment planning for breast cancer patient. The breast cancer sample images were gathered from the city's hospitals Chennai 2023 as well as the mini-MIAS database and DDSM database. The BoVW classifier is used to identify and classify breast tumor images according to their size. This improves the efficacy of the classification method developed for the CAD system's mammography breast cancer picture categorization. Several Computer Aided Diagnosis (CAD) systems have been created to use mammography images to identify breast cancer in its early stages. ANN attained 93.6%,94.18%, 93.2% and SVM gained 92%,92.44%,93.02% for Precision, Recall and Accuracy Respectively.The proposed method display excellent accuracy of 98.83% over the other methods. The novelty could stem from the seamless integration of multiple modalities within the feature extraction and classification framework.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society’s (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Enhanced Breast Cancer Detection Using Modified Particle Swarm Optimization-Based Back Propagation Neural Network

Breast cancer detection faces challenges due to late diagnosis and inaccurate results, leading to improper treatment. Particle Swarm Optimization (PSO), a common optimization technique, often gets trapped in local optima, resulting in high false negatives and computational time. To address this, a study devised a modified PSO-based Back Propagation Neural Network (BPNN) using White Shark Optimization (WSO) for more accurate breast cancer detection. The research collected 1097 mammogram images from two medical centers and categorized them into benign, and malignant groups. Image preprocessing involved techniques like Otsu's method and contrast limited adaptive histogram equalization, with Fuzzy C Means used for segmentation. Region of Interest (ROI) extraction utilized Gabor filter and Local Binary Pattern, followed by weighted average fusion of textural features. WSPSO was then applied for optimal feature selection, and BPNN classified the identified features. MATLAB R2016a implemented the system. Evaluation metrics included accuracy, precision, sensitivity, specificity, False Positive Rate (FPR), and Computation Time (CT). Compared to PSO-BPNN, WSPSO-BPNN demonstrated superior performance. The accuracy, precision, sensitivity, specificity, FPR and CT of WSPSO-BPNN for malignant dataset were 96.88%, 99.08%, 96.43%, 97.92%, 2.08%, and 32.38 seconds, respectively, while the corresponding values for PSO-BPNN were 93.13%, 96.33%, 93.75%, 91.67%, 8.33% and 44.03 seconds, respectively. Also for the accuracy, precision, sensitivity, specificity, FPR and CT of WSPSO-BPNN for benign dataset were 96.15%, 98.14%, 96.34%, 95.71%, 4.29%, and 40.04 seconds, respectively, while the corresponding values for PSO-BPNN were 93.59%, 96.27%, 94.51%, 91.43%, 8.57% and 61.94 seconds respectively showcasing its potential for improving breast cancer diagnosis. Keywords: Back Propagation Neural Network, Breast Cancer, Classification, Mammogram Images, Particle Swarm Optimization, White Shark Optimization. Oyerinde, I.M., Olabiyisi, S.O., Falohun, A.A. & Awodoye, O.O. (2024); Enhanced Breast Cancer Detection Using Modified Particle Swarm Optimization-Based Back Propagation Neural Network. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 53-62. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P5

Evaluation of Brest cancer by two different modalities Ultrasound and Mammography

Ultrasound is an inexpensive, simple test that doesn't cause any discomfort or anxiety from claustrophobia as an Mammography might. Ultrasound tests also don't emit any radiation, so they are safe for just about everyone. They also offer very clear images, better than those produced by an X-ray. Ultrasound can provide a more precise diagnosis than a mammogram alone and can better detect very small abnormalities and those in dense breasts. The main objective is the Confirmation of breast cancer by ultrasound, determined false negative by comparison with histopathology report and determined false positive by comparison with histopathology report.A hundred cases were present at oncology institute, they were divided in to tow group 50 were above 35y old and the other one younger than that, first group were examined by ultrasound and mammography, while the second group were examined by ultrasound only.The study showed that about 72% of cases were having malignancy by histopathology, while the other had malignancy by either ultrasound or mammography.28 % were found to be benign case.In conclusion , the accuracy of breast cancer detection by ultrasound helps in decreasing waiting time for histopathological report. Therefore, ultrasound is found to be useful tool to evaluate high risk patient’s especially in young ages.

Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis.

Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.

Machine Learning Techniques for Breast Cancer Prediction: A Concise Review

Breast cancer is the most trending type of cancer globally with close to two and half million cases recorded based on research by the World Health Organization in 2021 and it is also the most common cancer among women in all countries, posing a major cause for public health concern. In Nigeria and the world at large, over a hundred thousand new cases of cancer occur every year with high death among women. It has been researched that early and accurate detection of breast cancer can aid in the diagnosis of the disease for women and it may also reduce the risk of death rate among women. Literature shows that several machine learning techniques have been carried out on breast cancer diagnosis to help provide accurate technology solutions to early detection. The machine learning techniques used have different accuracy rate which varies for dissimilarity conditions. In this study, we compared different methods with many existing machine-learning techniques commonly used for breast cancer detection and diagnosis. Also, the aim of this review method will show an improvement in accuracy performances by implementing different methods and analysis in existing machine learning techniques to proficiently assist doctors in decision-making on an accurate detection and diagnosis of breast cancer and classifying tumors as benign or malignant thereby reducing the risk of death rate among women.

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.

AND-gated logic detection of dual miRNAs in living cells and tissues via 3D catalytic hairpin assembly

The development of a DNA-based logic device capable of simultaneously detecting two microRNAs (miRNAs) has the potential to enhance the accuracy of cancer diagnosis. However, existing approaches are hindered by slow response times, limited sensitivity, and inadequate cellular delivery. In this study, we integrated logic operations with 3D catalytic hairpin assembly (3D-CHA) to devise a sensing system for the AND-gated analysis of miR-21 and miR-155 in living cells and tissues. We utilized two DNA nanospheres, designated AN1 and AN2, which, upon the presence of both miRNA inputs, release an initiator via strand displacement, consequently triggering 3D-CHA between AN1 and AN2 and generating an amplified output. Our findings demonstrate that the AN nanoprobe facilitates the logical analysis of dual miRNAs in buffers, living cells, and tissues, offering effective cellular delivery, nuclease resistance, heightened sensitivity, and rapid response time. The AN nanoprobe represents a promising diagnostic tool for the accurate detection of breast cancer and exhibits considerable potential for clinical applications.

Enhancing the Accuracy of Breast Cancer Detection with a Hybrid Clustering Algorithm Combining K-Means and GMM

Abstract: Breast cancer is among the most widespread ailments afflicting women globally. The timely identification and accurate diagnosis are vital for successful therapy and improved prognosis. Over the past years, researchers have extensively utilized machine learning algorithms to detect breast cancer from medical images. This paper proposes an innovative hybrid clustering approach combining both k-means and Gaussian mixture models (GMM) to enhance breast cancer detection performance. By utilizing the k-means clustering approach as a basis, our algorithm generates initial cluster centers from the input data. With these results, we then proceed to implement the GMM algorithm to further refine our clustering outcomes and calculate each cluster's probability distribution accordingly. Through evaluation on publicly available mammography images, our hybrid algorithm outperformed both k-means and GMM algorithms in terms of sensitivity, specificity and area under the receiver operating characteristic curve (AUC-ROC).

BUS‐CAD: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

AbstractBreast cancer has become the most prominent type of cancer in the world. Early detection of breast cancer plays an important role in optimal treatment planning to decrease mortality. Breast ultrasound is widely used in diagnosing breast masses. Applications of machine learning in ultrasound imaging‐based classification have shown promising potential for early and accurate detection of breast cancer. In this study, a new computer‐aided diagnosis system based on machine learning techniques for breast cancer classification is proposed. Feature space is extended by using hybrid feature representations that combine both global and local texture statistics. A two‐step feature selection process is implemented using Boruta all‐relevant feature selection algorithm and iterative correlation analysis. A grid‐search strategy is followed along with 20 times repeated 10‐fold randomly stratified cross‐validation to optimize machine learning algorithms. Fourteen classification models based on random forest (RF) and support vector machine trained using all combinations of global features and the features driven from gray‐level co‐occurrence matrix and local binary patterns are tested. The experiments showed that the RF classifier on the hybrid feature vector that combines all global and local features achieved the best classification performance with average accuracy and area under the curve of 97.81% and 99.80%, respectively. The results suggest that the proposed system efficiently improves the classification performance of breast lesions on ultrasound images and can assist clinical decision‐making.

DeepBreastCancerNet: A Novel Deep Learning Model for Breast Cancer Detection Using Ultrasound Images

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.