scholarly journals Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study

2019 ◽  
Vol 5 (3) ◽  
pp. 37 ◽  
Author(s):  
Lazaros Tsochatzidis ◽  
Lena Costaridou ◽  
Ioannis Pratikakis
Keyword(s):  
Breast Cancer ◽  
State Of The Art ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Superior Performance ◽  
Deep Convolutional Neural Networks ◽  
Random Fashion ◽  
Mass Lesions ◽  
Aided Diagnosis ◽  
Malignant Mass

Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch.

scholarly journals An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

2021 ◽  
Vol 9 ◽  
Author(s):  
Yifeng Dou ◽  
Wentao Meng
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Medical Institutions ◽  
High Level ◽  
Aided Diagnosis ◽  
Very High ◽  
Important Branch

As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.

Breast Cancer Diagnosis With Mammography

2021 ◽  
pp. 107-127
Author(s):  
Abir Baâzaoui ◽  
Walid Barhoumi
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Gold Standard ◽  
Breast Cancer Diagnosis ◽  
Black Box ◽  
Diagnose Breast Cancer ◽  
Cad Systems ◽  
Helpful Tool ◽  
Cancerous Lesion ◽  
Aided Diagnosis

Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an increase in missed cancers and misinterpreted non-cancerous lesion rates. Therefore, computer-aided diagnosis (CAD) systems can be a great helpful tool for assisting radiologists in mammogram interpretation. Nonetheless, these systems are limited by their black-box outputs, which decreases the radiologists' confidence. To circumvent this limit, content-based mammogram retrieval (CBMR) is used as an alternative to traditional CAD systems. Herein, authors systematically review the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advances in CBMR methods. In order to have a complete review, mammography imaging principles and its correlation with breast anatomy are also discussed.

scholarly journals A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Lian Zou ◽  
Shaode Yu ◽  
Tiebao Meng ◽  
Zhicheng Zhang ◽  
Xiaokun Liang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Learning Tools ◽  
Pros And Cons ◽  
Future Work

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network

2020 ◽  
Author(s):  
Amin Rezaeipanah ◽  
Gholamreza Ahmadi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Fine Tuning ◽  
Hybrid Classifier ◽  
Mlp Neural Network ◽  
Multi Stage ◽  
Hybrid Classification ◽  
Diagnosis Technique

Abstract Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.

scholarly journals An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment

2019 ◽  
Vol 9 (4) ◽  
pp. 20190034 ◽  
Author(s):  
Thiranja Prasad Babarenda Gamage ◽  
Duane T. K. Malcolm ◽  
Gonzalo Maso Talou ◽  
Anna Mîra ◽  
Anthony Doyle ◽  
...  
Keyword(s):  
Breast Cancer ◽  
State Of The Art ◽  
Three Dimensional ◽  
Breast Cancer Diagnosis ◽  
Automated System ◽  
Machine Learning Techniques ◽  
Breast Cancers ◽  
Computational Biomechanics ◽  
Biomechanical Models ◽  
Treatment Procedures

Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.

Improving breast cancer diagnosis with computer-aided diagnosis

1999 ◽  
Vol 6 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Yulei Jiang ◽  
Robert M. Nishikawa ◽  
Robert A. Schmidt ◽  
Charles E. Metz ◽  
Maryellen L. Giger ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Md Zahangir Alom ◽  
Paheding Sidike ◽  
Mahmudul Hasan ◽  
Tarek M. Taha ◽  
Vijayan K. Asari
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
State Of The Art ◽  
The State ◽  
Superior Performance ◽  
Deep Convolutional Neural Networks ◽  
Practical Applications ◽  
High Degree

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

Medical image based breast cancer diagnosis: State of the art and future directions

2020 ◽  
pp. 114095
Author(s):  
Mehreen Tariq ◽  
Sajid Iqbal ◽  
Hareem Ayesha ◽  
Ishaq Abbas ◽  
Khawaja Tehseen Ahmad ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Medical Image ◽  
State Of The Art ◽  
Breast Cancer Diagnosis ◽  
Future Directions

Advanced Fuzzy Methods for Mammography Image Analysis

2012 ◽  
pp. 107-120
Author(s):  
Farhang Sahba ◽  
Anastasios Venetsanopoulos ◽  
Gerald Schaefer
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Incidence ◽  
Breast Cancer Diagnosis ◽  
Classification Problem ◽  
Computer Aided Diagnosis ◽  
Incidence Rates ◽  
Fuzzy Methods ◽  
Computer Aided ◽  
Inexact Data ◽  
Aided Diagnosis

Breast cancer is the second most common type of cancer worldwide and one of the most common causes of cancer deaths. Worryingly, breast cancer incidence rates have increased over recent years. Computer Aided Diagnosis (CADx) systems are designed to help radiologists identify cancerous signs earlier, and hence to reduce the death rate. These systems involve at least two main stages: feature extraction to derive useful information from the images, and diagnosis which is typically handled as a machine learning/pattern classification problem. For breast cancer diagnosis, x-ray mammography is the main modality of diagnosis. The inherent fuzziness in the nature of mammography images makes fuzzy set theory a useful technique for handling these images. It is used as a well-suited tool to extract meaningful information from inexact data and generate appropriate solutions. In this chapter, the authors present a fast overview of some fuzzy-based methods for computer-aided detection and computer aided diagnosis of breast cancer using mammography images. Their focus is on fuzzy logic-based methods developed for mammogram enhancement, microcalcification (MC) detection, and detection and classification of masses.

scholarly journals Nuclei segmentation for computer-aided diagnosis of breast cancer

2014 ◽  
Vol 24 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Marek Kowal ◽  
Paweł Filipczuk
Keyword(s):  
Breast Cancer ◽  
Color Space ◽  
Digital Pathology ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Decision ◽  
Fast Marching ◽  
Fine Needle ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.

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