Algorithm For Breast Cancer Detection Research Articles

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

Optimized Federated Learning Algorithm for Breast Cancer Detection Using the Marine Predators Algorithm

Breast cancer stands as the leading cause of cancer-related fatalities among women worldwide, necessitating effective detection methods. However, the current landscape of breast cancer detection poses several challenges, including high costs, complexity, and suboptimal accuracy rates. In response, this paper presents a pioneering approach to breast cancer detection leveraging federated learning, a paradigm that allows model training across distributed data sources without centralized data sharing. Central to this novel detection model is the integration of a Convolutional Neural Network (CNN) architecture and a meta-heuristic optimization algorithm known as the marine predator's algorithm (MPA). CNNs have emerged as powerful tools for biomedical image analysis, enabling automatic feature extraction crucial for detecting abnormalities in medical images. However, optimizing CNNs for detection tasks requires meticulous tuning of hyperparameters across different layers, a process impractical to perform manually due to its complexity and time-intensive nature. To address this challenge, the paper proposes the utilization of the MPA as an optimization algorithm within the federated learning framework. The MPA, inspired by the foraging behaviour of marine predators, enhances the exploration-exploitation trade-off to prevent the model from getting stuck in local optima during training. By incorporating the MPA into the CNN architecture, the proposed model achieves remarkable testing accuracy, surpassing the performance of traditional DCNN models. During testing, the proposed model attained an impressive accuracy rate of 98.32%, showcasing its superior performance compared to the DCNN model, which achieved a 95.95% accuracy. Moreover, the proposed approach outperforms existing algorithms, achieving an accuracy rate of 98%. These results underscore the effectiveness of the federated learning-based approach in enhancing breast cancer detection accuracy. the proposed breast cancer detection model represents a significant advancement in the field, offering a highly accurate and efficient solution to overcome the challenges associated with traditional detection methods. By leveraging federated learning and innovative optimization techniques, this model holds promise for improving early detection rates, ultimately contributing to better patient outcomes in the fight against breast cancer

An enhanced multi-scale deep convolutional orchard capsule neural network for multi-modal breast cancer detection

Breast cancer is the second-leading cause of cancer death in women. Breast cells develop into malignant, cancerous lumps, the first signs of breast cancer. Breast cancer can be discovered by the automated diagnostic system when it is still too little to be found by conventional medical methods. Early breast cancers identified with automated screening and diagnosis technologies are generally treatable. This study proposes an enhanced multi-scale deep Convolutional Capsule Neural Network (CapsNet) optimized with Orchard Optimization Algorithm for breast cancer detection. The proposed system consists of preprocessing, feature extraction, segmentation, and classification process. Two input images are taken initially: the Breast Cancer Histopathology Images dataset and the Infrared Thermal Images dataset. The quality of the collected data is improved, and unwanted noises are removed. The features are extracted to segment the image to derive a Region of Interest for effectively segmenting the affected region. Finally, the images are classified as benign/malignant for histopathology images and healthy/cancer for thermal images. The proposed CapsNet is implemented in Python, run for 200 epochs, and compared with existing methods in terms of evaluation metrics. The result shows that the proposed CapsNet attained 99.74 % accuracy, 0.0482 binary entropy loss on the Breast Cancer Histopathology Image dataset and 97 % accuracy, 0.2081 binary entropy loss on the Infrared Thermal Images dataset while incrementing the epochs at each level.

A Novel Medical Image Enhancement Algorithm for Breast Cancer Detection on Mammography Images Using Machine Learning.

Mammography is the most preferred method for breast cancer screening. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography images because pre-processing algorithms significantly affect the accuracy of segmentation and classification methods. In this study, the effect of combinations of different preprocessing methods in differentiating benign and malignant breast lesions was investigated. All image processing algorithms used for lesion detection were used in the mini-MIAS database. In the first step, label information and pectoral muscle resulting from the acquisition of mammography images were removed. In the second step, median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) algorithms with different combinations of the resolution and visibility of images are increased. In the third step, suspicious regions are extracted from the mammograms using the k-means clustering technique. Then, features were extracted from the obtained ROIs. Finally, feature datasets were classified as normal/abnormal, and benign/malign (two class classification) using Machine Learning algorithms. Test performance measures of the classification methods were examined. In both classifications made in the study, lower classification performance values were obtained when the CLAHE algorithm was used alone as a pre-processing method compared to other pre-processing combinations. When the median filter and unsharp masking algorithms are added to the CLAHE algorithm, the performance of the classification methods has increased. In terms of classification success, Support Vector Machines, Random Forest, and Neural Networks showed the best performance. It was found by comparing the performances of the classification methods that different preprocessing algorithms were effective in detecting the presence of breast lesions and distinguishing benign and malignant.

Breast Cancer Detection in Mammogram Images Using K-Means++ Clustering Based on Cuckoo Search Optimization.

Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high-quality features, which can result in very long processing times. Therefore, this paper proposes a new K-means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU's threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini-MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score.

Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)—based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms

Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

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.

Sign coherence factor‐based double‐stage delay multiply and sum beamforming algorithm for breast cancer detection

AbstractThe delay‐multiply‐and‐sum (DMAS) beamforming algorithm has been extensively used in medical imaging. In this study, we proposed a fast double‐stage DMAS beamformer for breast cancer detection, combined with the sign coherence factor (SCF), namely SCF‐DMAS. The performance of this method was evaluated on a three‐dimensional‐printed breast phantom surrounded by evenly spaced circular array antennas. The results show that the proposed algorithm outperforms the other beamformers involved with respect to the signal‐to‐clutter Ratio, signal‐to‐mean ratio, and localization error. To summarize, the SCF‐DMAS improves the resolution and sidelobe suppression without significantly increasing the computational complexity.

Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.

Breast cancer is one of the most widespread diseases in women worldwide. It leads to the second-largest mortality rate in women, especially in European countries. It occurs when malignant lumps that are cancerous start to grow in the breast cells. Accurate and early diagnosis can help in increasing survival rates against this disease. A computer-aided detection (CAD) system is necessary for radiologists to differentiate between normal and abnormal cell growth. This research consists of two parts; the first part involves a brief overview of the different image modalities, using a wide range of research databases to source information such as ultrasound, histography, and mammography to access various publications. The second part evaluates different machine learning techniques used to estimate breast cancer recurrence rates. The first step is to perform preprocessing, including eliminating missing values, data noise, and transformation. The dataset is divided as follows: 60% of the dataset is used for training, and the rest, 40%, is used for testing. We focus on minimizing type one false-positive rate (FPR) and type two false-negative rate (FNR) errors to improve accuracy and sensitivity. Our proposed model uses machine learning techniques such as support vector machine (SVM), logistic regression (LR), and K-nearest neighbor (KNN) to achieve better accuracy in breast cancer classification. Furthermore, we attain the highest accuracy of 97.7% with 0.01 FPR, 0.03 FNR, and an area under the ROC curve (AUC) score of 0.99. The results show that our proposed model successfully classifies breast tumors while overcoming previous research limitations. Finally, we summarize the paper with the future trends and challenges of the classification and segmentation in breast cancer detection.

Multi-Objective Feature Selection Method by Using ACO with PSO Algorithm for Breast Cancer Detection

Multi-Objective Feature Selection Method by Using ACO with PSO Algorithm for Breast Cancer Detection

Supervised Bayesian learning for breast cancer detection in terahertz imaging

This paper proposes a supervised multinomial Bayesian learning algorithm for breast cancer detection using terahertz (THz) imaging of freshly excised murine tumors. The proposed algorithm utilizes a multinomial Bayesian probit regression approach, which establishes the link between THz data and classification results by using two different models, a polynomial regression model and a kernel regression model. Such a model-based learning approach employs only a small number of model parameters, thus it requires much less training data when compared with alternative deep learning methods. The training phase of the algorithm is performed by using the histopathology results of formalin-fixed, paraffin embedded (FFPE) samples as ground truth. There is usually a considerable shape mismatch between the freshly excised sample and its FFPE counterpart due to sample dehydration, and such mismatch negatively impacts the quality of the training data. We propose to address this challenge by using an innovative reliability-based training data selection method, where the reliability of the training data is quantified and estimated by using an unsupervised expectation maximization (EM) classification algorithm with soft probabilistic output. Experiment results demonstrate that the proposed multinomial Bayesian probit regression models with reliability-based training data selection achieve better performance than existing methods. Overall, these results demonstrate that the proposed supervised segmentation models represent a promising technique for the region detection with THz imaging of freshly excised breast cancer samples.

Abstract 181: High-accuracy breast cancer detection in mammography using EfficientNet and end-to-end training

Abstract Background:Breast cancer (BC) is the second most common cancer among women. BC screening is usually based on mammography interpreted by radiologists. Recently, some researchers have used deep learning to automatically diagnose BC in mammography and so assist radiologists. The progress of BC detection algorithms can be measured by their performance on public datasets. The CBIS-DDSM is a widely used public dataset composed of scanned mammographies, equally divided into malignant and non-malignant (benign) images. Each image is accompanied by the segmentation of the lesion. Shen et al. (Nature Sci. Rep., 2019) presented a BC detection algorithm using an “end-to-end” approach to train deep neural networks. In this algorithm, a patch classifier is first trained to classify local image patches. The patch classifier's weights are then used to initialize the whole image classifier, that is refined using datasets with the cancer status of the whole image. They achieved an AUC of 0.87 [0.84, 0.90] in classifying CBIS-DDSM images, using their best single-model, single-view breast classifier. They used ResNet (He et al., CVPR 2016) as the basis of their algorithm. Our hypothesis was that replacing the old ResNet with the modern EfficientNet (Tan et al., arXiv 2019) and MobileNetV2 (Sandler et al.,CVPR 2018) would result in greater accuracy. Methods:We tested many different models, to conclude that the best model is obtained using EfficientNet-B4 as the base model, with a MobileNetV2 block at the top, followed by a dense layer with two output categories. We trained the patch classifier using 52,528 patches with 224x224 pixels extracted from CBIS-DDSM. From each image, we extracted 20 patches: 10 patches containing the lesion and 10 from the background (without lesion). The patch classifier weights were then used to initialize the whole image classifier, that was trained using the end-to-end approach with CBIS-DDSM images resized to 1152x896 pixels, with data augmentation. The training was performed using a step learning rate of 1e-4 for the first 20 epochs then 1e-5 for the remaining 10 and batch size of 4, using 10-fold cross-validation. We used 81% of the dataset for training, 9% for validation and 10% for testing. Results:We obtained an AUC of 0.8963±0.06, using a single-model, single-view classifier and without test-time data augmentation. Conclusions:Using EfficientNet and MobileNetV2 as the basis of the BC detection algorithm (instead of ResNet), we obtained an improvement in classifying CBIS-DDSM images into malignant/non-malignant: AUC has increased from 0.87 to 0.896. Our AUC is also larger than other recent papers in the literature, such as Shu et al. (IEEE Trans Med. Image, 2020) that achieved an AUC of 0.838 in the same CBIS-DDSM dataset. Citation Format: Daniel G. Petrini, Carlos Shimizu, Gabriel V. Valente, Guilherme Folgueira, Guilherme A. Novaes, Maria L. Katayama, Pedro Serio, Rosimeire A. Roela, Tatiana C. Tucunduva, Maria Aparecida A. Folgueira, Hae Y. Kim. High-accuracy breast cancer detection in mammography using EfficientNet and end-to-end training [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 181.

AI-based imaging biomarker in mammography for prediction of tumor invasiveness.

1568 Background: The preoperative diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) can be upstaged in the final pathology, and this possibility is linked to the controversy over whether axillary staging is necessary in primary operation. In this study, we developed an artificial intelligence (AI)-powered Imaging Biomarker in Mammography (IBM) that can predict tumor invasiveness in preoperative mammography and evaluated its performance in an external validation cohort. Methods: A total of 151,764 exams of 4-view mammograms were collected from five institutions of three countries to develop the AI algorithm for breast cancer detection, where 31,776 were cancer exams. In previous studies, the performance of this breast cancer detection algorithm has already been evaluated, and in this study, we further developed the AI-powered IBM for predicting tumor invasiveness on top of the AI algorithm for breast cancer detection. To develop the AI-powered IBM for predicting invasiveness, final diagnosis information was collected for 8,251 cancer exams (472 DCIS, 388 ductal carcinoma in situ (DCIS-MI), and 7,391 invasive ductal carcinoma (IDC)), and 886 cancer exams (44 DCIS, 51 DCIS-MI, 791 IDC) were additionally collected for internal validation. The AI-powered IBM was developed via two stages of training – 1) training with diagnosis labels (cancer vs non-cancer), followed by 2) fine-tuning with invasiveness labels (DCIS, DCIS-MI, IDC). The AI-powered IBM also tested in an external validation cohort of 699 cancer exams (68 DCIS, 19 DCIS-MI, 612 IDC) and all the exams were confirmed by surgical biopsy. Results: The AI-powered IBM showed an area under the curve (AUC) values of 0.968 for breast cancer detection and it successfully distinguished IDC from DCIS and DCIS-MI with AUC values of 0.898 and 0.851, respectively (Table). In addition, the AUC value in terms of discriminating between DCIS and DCIS-MI was 0.752. When the AI-powered IBM was tested in the external cohort, it could detect breast cancer with the AUC of 0.952 and, its performance in terms of invasiveness prediction was similar that of the internal validation (IDC vs DCIS, 0.810; IDC vs DCIS-MI, 0.846), which supports the AI-powered IBM is applicable to the unseen mammography exam. Conclusions: The AI-powered IBM can distinguish IDC from DCIS and DCIS-MI in mammography. The results support that the AI-powered IBM can be used as a biomarker to help determine the surgical plan that includes whether or not to perform the axillary dissection.[Table: see text]

Surrogate-assisted firefly algorithm for breast cancer detection

Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost.

Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

Breast cancer is one of the most dangerous diseases and the second largest cause of female cancer death. Breast cancer starts when malignant, cancerous lumps start to grow from the breast cells. Self-tests and Periodic clinical checks help to early diagnosis and thereby improve the survival chances significantly. The breast cancer classification is a medical method that provides researchers and scientists with a great challenge. Neural networks have recently become a popular tool in cancer data classification. In this paper, Deep Learning assisted Efficient Adaboost Algorithm (DLA-EABA) for breast cancer detection has been mathematically proposed with advanced computational techniques. In addition to traditional computer vision approaches, tumor classification methods using transfers are being actively developed through the use of deep convolutional neural networks (CNNs). This study starts with examining the CNN-based transfer learning to characterize breast masses for different diagnostic, predictive tasks or prognostic or in several imaging modalities, such as Magnetic Resonance Imaging (MRI), Ultrasound (US), digital breast tomosynthesis and mammography. The deep learning framework contains several convolutional layers, LSTM, Max-pooling layers. The classification and error estimation that has been included in a fully connected layer and a softmax layer. This paper focuses on combining these machine learning approaches with the methods of selecting features and extracting them through evaluating their output using classification and segmentation techniques to find the most appropriate approach. The experimental results show that the high accuracy level of 97.2%, Sensitivity 98.3%, and Specificity 96.5% has been compared to other existing systems.

Optimization of Random Forest Algorithm for Breast Cancer Detection

Optimization of Random Forest Algorithm for Breast Cancer Detection

Correction to “Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis”

In the above article [1] , Figure 4 was erroneously copied from the published article [2] . The original figure was meant to be used as a reference to create a new image using our algorithm. The original Figure 4 is similar to that in [1] . The correct Figure 4 is shown in the following illustration. In articles [1] and [2] , the same deep learning algorithms were adopted, and the same database was used resulting in the closely matched figures.

UWB Rectangular Microstrip Patch Antenna Design in Matching Liquid and Evaluating the Classification Accuracy in Data Mining Using Random Forest Algorithm for Breast Cancer Detection with Microwave

The most common type of cancer for a female is breast cancer in the world. Regular checks and effective-timely treatment are noteworthy parameters for patients’ survival struggle . Against existing imaging methods, microwave imaging method has been considered more powerful and effective method by many researchers. In this paper, comprehensive design equations and parameters of rectangular microstrip patch antenna (RMPA) are given for microwave breast cancer detection. The layered breast model with a spherical tumor that was placed into the fibro-glandular layer was created by using CST Microwave Studio Software, and it was embedded in canola oil to decrease the distorted signals between the transmitting and receiving antennas. The RMPA has a wideband performance from 3 to 18 GHz. The simulation results show that differences in the electric field and reflection coefficients might more efficiently give a possibility to assign the tumor in the breast model. In addition, in this study, the data obtained from these experiments are classified by using the random forest algorithm from the data mining methods. According to the classification result, the random forest algorithm can diagnose breast cancer by classifying the tumor as 94% accuracy.

Compressive sensing unmixing algorithm for breast cancer detection

Traditional breast cancer imaging methods using microwave imaging (MWI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. The authors describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content, high water content, and cancerous tissues, and the goal of the optimisation procedure is to recover the mixture proportions of each component. By utilising this approach in a hybrid digital breast tomosynthesis/MWI system, the unmixing reconstruction process can be posed as a sparse recovery problem such that compressive sensing techniques can be employed. A numerical analysis is performed, which demonstrates that cancerous lesions can be detected from their mixture proportion under the appropriate conditions.