Automated Breast Cancer Detection Research Articles

Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview.

The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of AI to improve early and accurate detection of this disease from histopathological images. For the implementation of the system, we utilized a customized architecture tailored to our context where image quality is low, based on the convolutional neural networks algorithm from the Keras library of TensorFlow. Subsequently, we developed a platform to facilitate its use. This article aims to present the methodology that was used and the results obtained.

Deep learning approaches for breast cancer detection in histopathology images: A review.

Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review.

The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.

A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection

Background: Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.Methods: In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.Results: Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.Conclusions: The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Multi-wavelength interference phase imaging for automatic breast cancer detection and delineation using diffuse reflection imaging

Millions of women globally are impacted by the major health problem of breast cancer (BC). Early detection of BC is critical for successful treatment and improved survival rates. In this study, we provide a progressive approach for BC detection using multi-wavelength interference (MWI) phase imaging based on diffuse reflection hyperspectral (HS) imaging. The proposed findings are based on the measurement of the interference pattern between the blue (446.6 nm) and red (632 nm) wavelengths. We consider implementing a comprehensive image processing and categorization method based on the use of Fast Fourier (FF) transform analysis pertaining to a change in the refractive index between tumor and normal tissue. We observed that cancer growth affects tissue organization dramatically, as seen by persistently increased refractive index variance in tumors compared normal areas. Both malignant and normal tissue had different depth data collected from it that was analyzed. To enhance the categorization of ex-vivo BC tissue, we developed and validated a training classifier algorithm specifically designed for categorizing HS cube data. Following the application of signal normalization with the FF transform algorithm, our methodology achieved a high level of performance with a specificity (Spec) of 94% and a sensitivity (Sen) of 90.9% for the 632 nm acquired image categorization, based on preliminary findings from breast specimens under investigation. Notably, we successfully leveraged unstained tissue samples to create 3D phase-resolved images that effectively highlight the distinctions in diffuse reflectance features between cancerous and healthy tissue. Preliminary data revealed that our imaging method might be able to assist specialists in safely excising malignant areas and assessing the tumor bed following resection automatically at different depths. This preliminary investigation might result in an effective "in-vivo" disease description utilizing optical technology using a typical RGB camera with wavelength-specific operation with our quantitative phase MWI imaging methodology.

Automated Prognosis Marker Assessment in Breast Cancers Using BLEACH&STAIN Multiplexed Immunohistochemistry.

Prognostic markers in routine clinical management of breast cancer are often assessed using RNA-based multi-gene panels that depend on fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for an improved risk assessment. To enable automated prognosis marker detection (i.e., progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], GATA3, TROP2, HER2, PD-L1, Ki67, TOP2A), a framework for automated breast cancer identification was developed and validated involving thirteen different artificial intelligence analysis steps and an algorithm for cell distance analysis using 11+1-marker-BLEACH&STAIN-mfIHC staining in 1404 invasive breast cancers of no special type (NST). The framework for automated breast cancer detection discriminated normal glands from malignant glands with an accuracy of 98.4%. This approach identified that five (PR, ER, AR, GATA3, PD-L1) of nine biomarkers were associated with prolonged overall survival (p ≤ 0.0095 each) and two of these (PR, AR) were found to be independent risk factors in multivariate analysis (p ≤ 0.0151 each). The combined assessment of PR-ER-AR-GATA3-PD-L1 as a five-marker prognosis score showed strong prognostic relevance (p < 0.0001) and was an independent risk factor in multivariate analysis (p = 0.0034). Automated breast cancer detection in combination with an artificial intelligence-based analysis of mfIHC enables a rapid and reliable analysis of multiple prognostic parameters. The strict limitation of the analysis to malignant cells excludes the impact of fluctuating tumor purity on assay precision.

Automatic detection of breast cancer for mastectomy based on MRI images using Mask R-CNN and Detectron2 models

Automatic detection of breast cancer for mastectomy based on MRI images using Mask R-CNN and Detectron2 models

A Short Review: Semantic Segmentation for Breast Cancer Detection in MRI Images

Automated breast cancer detection is of paramount importance as it could potentially reduce false-negative examinations and allow for earlier treatments, which would have a significant impact on patients and healthcare system costs. However, insufficient data, imbalanced classes, the detection of cancer in dense breasts, and the difficulty in distinguishing breast lymph nodes hinder the application of automated methods. To address these problems, in this paper, we presented an overview of deep learning models for the segmentation of breast cancer at an early stage by examining the contrast-enhanced MRI images. We examined the effectiveness of supervised machine learning models (UNet, VNet, SegResNet, and HighResNet) in the detection of breast cancer. We used 3D and 2.5D semantic segmentation models for pixel-level segmentation, which overcame the drawbacks of region-based segmentation. All the models are trained, validated, and tested on a private dataset collected from the Champalimaud Foundation, Portugal, and evaluated quantitatively and qualitatively.

Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms

Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms

An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection

An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection

Enhanced mammogram classification with convolutional neural network: An improved algorithm for automated breast cancer detection

The current use of medical image processing can be employed to detect various diseases or tumors present in the human body. Breast cancer, which predominantly affects women and can result in death, can be identified at an early stage using mammography, a special screening method of computer tomography (CT) scan, which may potentially save the patient's life. Despite advancements in medical imaging technology, radiologists encounter difficulties in precisely discriminating between non-cancerous and cancerous brain tumors owing to their cellular composition. This study aims to solve the issue of automatic detection of breast cancer utilizing efficient image processing methods. The quality of mammography pictures is increased using Non-Local-Means (NLM) filtering, adaptive histogram equalization (AHE), and clustering with a morphological technique. The Gray Level Run Length Matrix (GLRLM), which considers the shape, surface, and location of the breast tumour cells, is utilized to extract important details from the mammography image. The K-nearest neighbor (K-NN) method is used to categorize the image as normal or abnormal based on the selected attributes. Using a kernel-based convolutional neural network, the tumour is then isolated from the aberrant image and categorized as benign or malignant (KBCNN). This approach has demonstrated improved precision compared to existing methods, achieving more than 95% accuracy.

A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.

Applying dual models on optimized LSTM with U-net segmentation for breast cancer diagnosis using mammogram images

Background of the studyBreast cancer is the most fatal disease that widely affects women. When the cancerous lumps grow from the cells of the breast, it causes breast cancer. Self-analysis and regular medical check-ups help for detecting the disease earlier and enhance the survival rate. Hence, an automated breast cancer detection system in mammograms can assist clinicians in the patient's treatment. In medical techniques, the categorization of breast cancer becomes challenging for investigators and researchers. The advancement in deep learning approaches has established more attention to their advantages to medical imaging issues, especially for breast cancer detection. AimThe research work plans to develop a novel hybrid model for breast cancer diagnosis with the support of optimized deep-learning architecture. MethodsThe required images are gathered from the benchmark datasets. These collected datasets are used in three pre-processing approaches like “Median Filtering, Histogram Equalization, and morphological operation”, which helps to remove unwanted regions from the images. Then, the pre-processed images are applied to the Optimized U-net-based tumor segmentation phase for obtaining accurate segmented results along with the optimization of certain parameters in U-Net by employing “Adapted-Black Widow Optimization (A-BWO)”. Further, the detection is performed in two different ways that is given as model 1 and model 2. In model 1, the segmented tumors are used to extract the significant patterns with the help of the “Gray-Level Co-occurrence Matrix (GLCM) and Local Gradient pattern (LGP)”. Further, these extracted patterns are utilized in the “Dual Model accessed Optimized Long Short-Term Memory (DM-OLSTM)” for performing breast cancer detection and the detected score 1 is obtained. In model 2, the same segmented tumors are given into the different variants of CNN, such as “VGG19, Resnet150, and Inception”. The extracted deep features from three CNN-based approaches are fused to form a single set of deep features. These fused deep features are inserted into the developed DM-OLSTM for getting the detected score 2 for breast cancer diagnosis. In the final phase of the hybrid model, the score 1 and score 2 obtained from model 1 and model 2 are averaged to get the final detection output. ResultsThe accuracy and F1-score of the offered DM-OLSTM model are achieved at 96 % and 95 %. ConclusionExperimental analysis proves that the recommended methodology achieves better performance by analyzing with the benchmark dataset. Hence, the designed model is helpful for detecting breast cancer in real-time applications.

An Improved Fully Automated Breast Cancer Detection and Classification System

An Improved Fully Automated Breast Cancer Detection and Classification System

Automatic breast cancer detection using HGMMEM algorithm with DELMA classification

Automatic breast cancer detection using HGMMEM algorithm with DELMA classification

Breast Cancer Detection Using Automated Segmentation and Genetic Algorithms.

Breast cancer is the most common cancer among women worldwide, after lung cancer. However, early detection of breast cancer can help to reduce death rates in breast cancer patients and also prevent cancer from spreading to other parts of the body. This work proposes a new method to design a bio-marker integrating Bayesian predictive models, pyRadiomics System and genetic algorithms to classify the benign and malignant lesions. The method allows one to evaluate two types of images: The radiologist-segmented lesion, and a novel automated breast cancer detection by the analysis of the whole breast. The results demonstrate only a difference of 12% of effectiveness for the cases of calcification between the radiologist generated segmentation and the automatic whole breast analysis, and a 25% of difference between the lesion and the breast for the cases of masses. In addition, our approach was compared against other proposed methods in the literature, providing an AUC = 0.86 for the analysis of images with lesions in breast calcification, and AUC = 0.96 for masses.

Two-Stage Feature Selection Method Created for 20 Neurons Artificial Neural Networks for Automatic Breast Cancer Detection

Breast cancer is a common deadly diseases in women. Initial recognition of breast cancer using mammogram images is a challenging task. Hence, this paper proposed a unique automatic diagnosis model for breast cancer. Initially, the mammogram images are preprocessed with a median filter and contrast limited adaptive histogram equalization (CLAHE). The pre-processed image is automatically segmented using the multilevel threshold method. Subsequently, statistical, texture, shape, and geometric features are extracted from the segmented image. So, the length of the feature vector is high, and it is important to identify optimum features. In this paper, the dimension of the feature vector is reduced by 2-stage feature selection methods. Initially, the feature vector is applied to the best first search method information gain (IG) with rank feature method, and then secondly, apply the Pearson correlation method (PCM). Artificial neural networks (ANNs) are used to increase the classification accuracy of a breast cancer diagnosis. In this model, the selection of appropriate neurons in a single hidden layer is used to avoid overfitting problems in an ANN model. Based on optimum feature selection, the appropriate number of neurons chosen in the hidden layer is 20, which was applied for the proposed IG+PCM+Boosted-ANN model. The proposed model is applied on 2 regular datasets mini-Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM). The proposed model was superior to other exiting models and the model in this study achieves the accuracy of 99 and 98.80 % for mini-MIAS and DDSM datasets, respectively.

Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms

ABSTRACT Breast cancer (BC) masses and microcalcification are nonlinear with complex dynamics due to which radiologists fail to properly diagnose breast cancer. In this paper, we used a hybrid features extracting approach based on texture, morphological, Scale Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), entropy, Elliptic Fourier Descriptors (EFDs), RICA, and sparse filtering methods. Various machine learning techniques have been employed to detect breast cancer, viz. Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbour, and Naïve Bayes classifiers. The RICA-based feature set using SVM RBF has resulted in total accuracy of (94.88%), and ROC AUC = 0.9914. The hybrid features using RICA have been computed with other combinatorial logics. Moreover, the highest performance to detect BC based on the fusion of features was obtained with RICA with Textural features using SVM Gaussian kernel and yielded a total accuracy of (97.55%), and ROC AUC = 0.9976. The hybrid features with RICA were found to yield the highest detection performance. It is revealed that the new feature-extracting approach can be useful for the early detection of breast cancer by physicians to decrease the overall mortality rate. The methods will be very useful for treatment modification to achieve better clinical outcomes.

Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm

In recent years, deep learning has been used to develop an automatic breast cancer detection and classification tool to assist doctors. In this paper, we proposed a three-stage deep learning framework based on an anchor-free object detection algorithm, named the Probabilistic Anchor Assignment (PAA) to improve diagnosis performance by automatically detecting breast lesions (i.e., mass and calcification) and further classifying mammograms into benign or malignant. Firstly, a single-stage PAA-based detector roundly finds suspicious breast lesions in mammogram. Secondly, we designed a two-branch ROI detector to further classify and regress these lesions that aim to reduce the number of false positives. Besides, in this stage, we introduced a threshold-adaptive post-processing algorithm with dense breast information. Finally, the benign or malignant lesions would be classified by an ROI classifier which combines local-ROI features and global-image features. In addition, considering the strong correlation between the task of detection head of PAA and the task of whole mammogram classification, we added an image classifier that utilizes the same global-image features to perform image classification. The image classifier and the ROI classifier jointly guide to enhance the feature extraction ability and further improve the performance of classification. We integrated three public datasets of mammograms (CBIS-DDSM, INbreast, MIAS) to train and test our model and compared our framework with recent state-of-the-art methods. The results show that our proposed method can improve the diagnostic efficiency of radiologists by automatically detecting and classifying breast lesions and classifying benign and malignant mammograms.

Improved FCOS for Detecting Breast Cancers.

Breast cancer ranks first among cancers affecting women's health. Our goal is to develop a fast, high-precision, and fully automated breast cancer detection algorithm to improve the early detection rate of breast cancer. We compare different object detection algorithms, including anchor-based and anchor-free object detection algorithms for detecting breast lesions. Finally, we find that the fully convolutional onestage object detection (FCOS) showed the best performance in the detection of breast lesions, which is an anchor-free algorithm. 1) Considering that the detection of breast lesions requires the context information of the ultrasound images, we introduce the non-local technique, which models long-range dependency between pixels to the FCOS algorithm, providing the global context information for the detection of the breast lesions. 2) The variety of shapes and sizes of breast lesions makes detection difficult. We propose a new deformable spatial attention (DSA) module and add it to the FCOS algorithm. The detection performance of the original FCOS is that the average precision (AP) for benign lesions is 0.818, and for malignant lesions is 0.888. The FCOS with a non-local module improves the performance of the breast detection; the AP of benign lesions was 0.819, and that of malignant lesions was 0.894. Combining the DSA module with the FCOS improves the performance of breast detection; the AP for benign lesions and malignant lesions is 0.840 and 0.899, respectively. We propose two methods to improve the FCOS algorithm from different perspectives to improve its performance in detecting breast lesions. We find that FCOS combined with DSA is beneficial in improving the localization and classification of breast tumors and can provide auxiliary diagnostic advice for ultrasound physicians, which has a certain clinical application value.