Review: Breast Cancer Detection Using Deep Learning

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.

Background: The merging of machine learning and deep learning approaches offers a viable route for improving breast cancer prediction in this era of advanced technology and data-driven healthcare. This study focused on the use of supervised machine learning and deep learning algorithms for breast cancer prediction using Wisconsin breast cancer dataset. Methods: The features from this dataset were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. Supervised machine learning algorithms, such as logistic regression, random forests, and support vector machines were compared with deep learning algorithms like convolutional and recurrent neural networks (RNN and CNN) for the prediction of breast cancer. The techniques combine clinical, genomic, and image-based data, that both aid in early diagnosis and individualized treatment suggestions. Results: The results of model comparison were very promising, with models obtaining excellent accuracy, sensitivity, and specificity in breast cancer diagnosis. The study used a pair plot and different classifiers with their accuracy scores on test dataset to determine which machine learning model was most suitable based on the relationship of variables represented in the plot. Support vector machine (SVM) has the highest accuracy of 98.8% and Artificial Neural Network (ANN) has lowest accuracy of 95.80% using confusion matrix and the accuracy metric. Conclusion: The study revealed that Support Vector Machine (SVM) has the highest prediction capability for Breast Cancer using Supervised Machine Learning and Deep Leaning. The new classifier performed better than the existing breast cancer prediction models in the literature.

The early and precise diagnosis of breast cancer is one of the most important factors in enhancing survival rates. Despite considerable improvements in screening methods, medical imaging, and treatment approaches, the issue of reliable early detection remains a significant problem due to instability in clinical interpretation and the complexity of diagnostic information. Conventional methods of diagnosis emphasize the analysis of clinical and histopathological information, which are prone to inter-observer error and do not necessarily reveal subtle patterns related to disease progression. Artificial Intelligence has demonstrated enormous potential in alleviating these weaknesses. Ensemble methods, as well as support vector machines and gradient boosting-based algorithms such as XGBoost and LightGBM, have shown excellent results in the analysis of structured clinical data. Simultaneously, deep learning (DL) systems, specifically convolutional neural networks (CNNs), have been demonstrated to be exceptionally successful in medical image analysis such as mammography and histopathology. However, the interpretability of such models remains a major obstacle to their usage in clinical practice. To address this problem, Explainable Artificial Intelligence (XAI) methods, including SHAP, LIME, and Grad-CAM, have been proposed to improve model transparency and contribute significantly to understanding how decisions are made. This paper reviews recent developments in ML, DL, and XAI in relation to breast cancer diagnosis. It examines commonly used datasets, evaluation metrics, and trends, and also reports the most significant issues, including data constraints, lack of interpretability, and challenges in multimodal integration. Moreover, this paper identifies knowledge gaps in existing research and highlights the necessity of developing robust, explainable, and actionable AI-based diagnostic systems.

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.

Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer.

As the network goes deep into all aspects of people''s lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

Cancer is the most deadly disease by nature. Breast cancer is the top cancer in women globally and led to crucial increase in cancer fatality. Initial stage cancers do not cause death in human beings. Excessive work is carried out for the early diagnosis so that patients can get successful treatment. Even though health authorities recommend regular screening with mammography in women, infrared thermography can be a potential adjunct mechanism for identifying breast cancer. Deep learning techniques have opened up a new age in the image classification especially in the computer aided medical image analysis. A novel attempt combining deep learning and thermography for early stage breast cancer diagnosis is proposed in this paper. A deep learning scheme is adopted for the classification of cancerous and normal thermal rat images based on the transfer learning technique. For extracting features multiple deep pre-trained networks are utilized and classification is done with a support vector machine. The proposed method is also compared with a standard softmax classifier.

As one of the most common malignant tumors among women worldwide, breast cancer requires early diagnosis and accurate classification to significantly improve patient survival rates. Conventional imaging techniques like mammography, ultrasound, and magnetic resonance imaging (MRI) play a pivotal role in breast cancer screening. Nonetheless, they are constrained by relatively low specificity and a significant dependence on the expertise of medical professionals. In recent years, machine learning and deep learning techniques have provided new approaches for the intelligent diagnosis of breast cancer by extracting high-dimensional features from medical images. This study delves into the pathological aspects, imaging technologies, and the implementation of machine learning algorithms in the context of breast cancer. It conducts a comprehensive review of the diagnostic criteria for non-invasive, early-stage invasive, and fully invasive breast cancers, while also evaluating the strengths and weaknesses of various imaging modalities, including mammography, ultrasound, MRI, and nuclear medicine imaging. The limitations of conventional imaging methods in subtype differentiation are also discussed. Furthermore, by integrating radiomics and deep learning models such as convolutional neural networks (CNN) and random forests, the study evaluates the performance of intelligent diagnostic systems in breast cancer classification. Clinical cases and publicly available datasets were used as data sources. The results show that combining multimodal imaging features with machine learning algorithms significantly improves diagnostic accuracy, achieving an area under the curve (AUC) of 0.922. This research provides theoretical support and technical references for the precise diagnosis and treatment of breast cancer. Future work should focus on enhancing model generalizability and conducting multi-center clinical validation.

One of the fatal diseases that occurs in women is breast cancer and is associated with late diagnosis and poor access to medical care according to the patient's needs, therefore neural networks play a relevant role in detection of breast cancer and aims to be a support to guarantee its accuracy and reliability in cancer results. Therefore, the aim of the present systematic review is to learn how neural networks help to improve accuracy in breast cancer diagnosis through image recognition. For this, the formula generated with the PICO methodology was used; Likewise, the first result was 203 investigations related to the topic and based on the established inclusion and exclusion criteria, 20 final free access scientific articles were selected from the Scopus database. In relation to the results, it was found that the use of neural networks in the diagnosis of breast cancer, especially convolutional neural networks (CNN), has proven to be a promising tool to improve the accuracy and early detection of the disease, reaching achieve an accuracy of 98 % in the recognition of clinical images, which means a big difference compared to traditional methods. On the other hand, although there are challenges such as the limited availability of high-quality data sets and bias in training data, it is suggested to investigate the development of methods that integrate multiple sources of information and the use of deep learning techniques.

BackgroundBreast cancer is the second leading cause of cancer related deaths in women worldwide. Reports about the early diagnosis of breast cancer are suggestive of an improved clinical outcome and overall survival rate in cancer patients. Therefore, cancer screening biomarker for early detection and diagnosis is urgently required for timely treatment and better cancer management. In this context, we investigated an association of cancer testis antigen, A-Kinase anchor protein 4 (AKAP4) with breast carcinoma.Methodology/FindingsWe first compared the AKAP4 gene and protein expression in four breast cancer cells (MCF7, MDA-MB-231, SK-BR3 and BT474) and normal human mammary epithelial cells. In addition, 91 clinical specimens of breast cancer patients of various histotypes including ductal carcinoma in situ, infiltrating ductal carcinoma and infiltrating lobular carcinoma and 83 available matched adjacent non-cancerous tissues were examined for AKAP4 gene and protein expression by employing in situ RNA hybridization and immunohistochemistry respectively. Humoral response against AKAP4 was also investigated in breast cancer patients employing ELISA. Our in vitro studies in all breast cancer cells revealed AKAP4 gene and protein expression whereas, normal human mammary epithelial cells failed to show any expression. Using in situ RNA hybridization and immunohistochemistry, 85% (77/91) tissue specimens irrespective of histotypes, stages and grades of breast cancer clinical specimens revealed AKAP4 gene and protein expression. However, matched adjacent non-cancerous tissues failed to display any AKAP4 gene and protein expression. Furthermore, humoral response was observed in 79% (72/91) of total breast cancer patients. Interestingly, we observed that 94% (72/77) of breast cancer patients found positive for AKAP4 protein expression generated humoral response against AKAP4 protein.ConclusionsCollectively, our data suggests that AKAP4 may be used as serum based diagnostic test for an early detection and diagnosis of breast cancer and may be a potential target for immunotherapeutic use.

In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occurs. Modern techniques are required for an accurate diagnosis of AD. Machine learning has gained attraction in the medical field as a means of determining a person's risk of developing AD in its early stages. One of the most advanced soft computing neural network-based Deep Learning (DL) methodologies has garnered significant interest among researchers in automating early-stage AD diagnosis. Hence, a comprehensive review is necessary to gain insights into DL techniques for the advancement of more effective methods for diagnosing AD. This review explores multiple biomarkers associated with Alzheimer's Disease (AD) and various DL methodologies, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), The k-nearest-neighbor (k-NN), Deep Boltzmann Machines (DBM), and Deep Belief Networks (DBN), which have been employed for automating the early diagnosis of AD. Moreover, the unique contributions of this review include the classification of ATN biomarkers for Alzheimer's Disease (AD), systemic description of diverse DL algorithms for early AD assessment, along with a discussion of widely utilized online datasets such as ADNI, OASIS, etc. Additionally, this review provides perspectives on future trends derived from critical evaluation of each variant of DL techniques across different modalities, dataset sources, AUC values, and accuracies.