scholarly journals Segmentation of Mammary Lesions in Ultrasound Images Applying Mask R-CNN

2019 ◽  
Author(s):  
Claudia Raquel Ibarrola Chamorro ◽  
Wagner Coelho de Albuquerque Pereira
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Fundamental Aspect ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Cad Systems ◽  
Cad System ◽  
Road Signs ◽  
The World ◽  
Computer Aided

Breast cancer is the most frequent malignant tumor in women and one of the most common in the world. One of the most important issues in this condition is early detection. Computer-aided diagnostic (CAD) systems are objects of research, aiming to provide a second opinion to the health professional. A fundamental aspect within the CAD system is the segmentation of the lesion, allowing an adequate extraction of the lesion characteristics. The use of a computerized segmentation method helps eliminate human variability and, consequently, improve the performance of the lesion classifier. Convolutional Neural Networks (CNNs) are being used in segmentation problems, such as various types of medical imaging, people and road signs detection, for example. So inspired by these promissing results, the present work has as main objective to analyze and implement the Mask R-CNN as a tool of segmentation of mammary lesions in images obtained by ultrasound, to propose an efficient method of segmentation, aiding in the classification process of the CAD systems.

scholarly journals A Research on Computer Aided Detection System for Women Breast Cancer Diagnosis from Digital Mammographic Images

2019 ◽  
Vol 8 (2S11) ◽  
pp. 1008-1014
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Death Rate ◽  
Computational Cost ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Mammographic Images ◽  
Computer Aided

The Women breast cancer is the most critical cancer that are found in women. Its the second important cause of death in the world. Breast cancer has been ranked number one cancer in Indian females with rates occurrence of 25.8 per 1,00,000 females and death rate 12.7 among 1,00,000. Generally breast cancer is a malignant tumor that begins in the cells of the breast and eventually it spreads to the surrounding tissues. Early detection and diagnosis can reduce the mortality rate. Radiologist misdiagnosis the disease due to technical issues such as imaging quality and human error. Radiologists can improve the performance of Computer Aided Detection/Diagnosis (CAD) systems to finding and discriminating between the normal and abnormal tissues. Breast cancer diagnosis can applied are applied recent CAD systems on imaging modalities such as mammogram, ultrasound, MRI and biopsy histopathological images. CAD system have four stages for diagnosis which are pre-processing, segmentation, Feature Extraction and Classification. CAD system are developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper focus on the survey of CAD system to detect women breast cancer disease from the digital mammographic images to achieve high accuracy and low computational cost.

scholarly journals Computer-Aided Assessment of Tumor Grade for Breast Cancer in Ultrasound Images

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Dar-Ren Chen ◽  
Cheng-Liang Chien ◽  
Yan-Fu Kuo
Keyword(s):  
Breast Cancer ◽  
Tumor Grade ◽  
Prognostic Indicators ◽  
Support Vector ◽  
Ultrasound Images ◽  
3 Dimensional ◽  
Cad System ◽  
Computer Aided ◽  
Malignant Breast ◽  
Aided Diagnosis

This study involved developing a computer-aided diagnosis (CAD) system for discriminating the grades of breast cancer tumors in ultrasound (US) images. Histological tumor grades of breast cancer lesions are standard prognostic indicators. Tumor grade information enables physicians to determine appropriate treatments for their patients. US imaging is a noninvasive approach to breast cancer examination. In this study, 148 3-dimensional US images of malignant breast tumors were obtained. Textural, morphological, ellipsoid fitting, and posterior acoustic features were quantified to characterize the tumor masses. A support vector machine was developed to classify breast tumor grades as either low or high. The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and anAZof 0.7940.

scholarly journals Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

2020 ◽  
Vol 10 (3) ◽  
pp. 2336 ◽  
Author(s):  
Susama Bagchi ◽  
Kim Gaik Tay ◽  
Audrey Huong ◽  
Sanjoy Kumar Debnath
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Imaging ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Computer Aided Detection ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.

Comparison of two software versions of a commercially available computer-aided detection (CAD) system for detecting breast cancer

2010 ◽  
Vol 51 (5) ◽  
pp. 482-490 ◽  
Author(s):  
Seung Ja Kim ◽  
Woo Kyung Moon ◽  
Soo-Yeon Kim ◽  
Jung Min Chang ◽  
Sun Mi Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Positive ◽  
Ductal Carcinoma ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Full Field ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Age Range

Background: The performance of the computer-aided detection (CAD) system can be determined by the sensitivity and false-positive marks rate, therefore these factors should be improved by upgrading the software version of the CAD system. Purpose: To compare retrospectively the performances of two software versions of a commercially available CAD system when applied to full-field digital mammograms for the detection of breast cancers in a screening group. Material and Methods: Versions 3.1 and 8.3 of a CAD software system (ImageChecker, R2 Technology) were applied to the full-field digital mammograms of 130 women (age range 36–80, mean age 53 years) with 130 breast cancers detected by screening. Results: The overall sensitivities of the version 3.1 and 8.3 CAD systems were 92.3% (120 of 130) and 96.2% (125 of 130) ( P=0.025), respectively, and sensitivities for masses were 78.3% (36 of 46) and 89.1% (41 of 46) ( P=0.024) and for microcalcifications 100% (84 of 84) and 100% (84 of 84), respectively. Version 8.3 correctly marked five lesions of invasive ductal carcinoma that were missed by version 3.1. Average numbers of false-positive marks per image were 0.38 (0.15 for calcifications, 0.23 for masses) for version 3.1 and 0.46 (0.13 for calcifications, 0.33 for masses) for version 8.3 ( P=0.1420). Conclusion: The newer version 8.3 of the CAD system showed better overall sensitivity for the detection of breast cancer than version 3.1 due to its improved sensitivity for masses when applied to full-field digital mammograms.

Machine Learning Applications in Breast Cancer Diagnosis

2017 ◽  
pp. 465-490
Author(s):  
Syed Jamal Safdar Gardezi ◽  
Mohamed Meselhy Eltoukhy ◽  
Ibrahima Faye
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Extraction Techniques ◽  
Cad Systems ◽  
Cad System ◽  
Machine Learning Applications ◽  
Detection And Diagnosis

Breast cancer is one of the leading causes of death in women worldwide. Early detection is the key to reduce the mortality rates. Mammography screening has proven to be one of the effective tools for diagnosis of breast cancer. Computer aided diagnosis (CAD) system is a fast, reliable, and cost-effective tool in assisting the radiologists/physicians for diagnosis of breast cancer. CAD systems play an increasingly important role in the clinics by providing a second opinion. Clinical trials have shown that CAD systems have improved the accuracy of breast cancer detection. A typical CAD system involves three major steps i.e. segmentation of suspected lesions, feature extraction and classification of these regions into normal or abnormal class and further into benign or malignant stages. The diagnostics ability of any CAD system is dependent on accurate segmentation, feature extraction techniques and most importantly classification tools that have ability to discriminate the normal tissues from the abnormal tissues. In this chapter we discuss the application of machine learning algorithms e.g. ANN, binary tree, SVM, etc. together with segmentation and feature extraction techniques in a CAD system development. Various methods used in the detection and diagnosis of breast lesions in mammography are reviewed. A brief introduction of machine learning tools, used in diagnosis and their classification performance on various segmentation and feature extraction techniques is presented.

Geometric Modelling and Computer-Aided Design

2009 ◽  
pp. 1-31
Author(s):  
Xun Xu
Keyword(s):  
Computer Aided Design ◽  
Data Exchange ◽  
Geometric Model ◽  
Point Of View ◽  
Geometric Modelling ◽  
Cad Systems ◽  
Cad System ◽  
Wire Frame ◽  
Computer Aided ◽  
Aided Design

One of the key activities in any product design process is to develop a geometric model of the product from the conceptual ideas, which can then be augmented with further engineering information pertaining to the application area. For example, the geometric model of a design may be developed to include material and manufacturing information that can later be used in computer-aided process planning and manufacturing (CAPP/CAM) activities. A geometric model is also a must for any engineering analysis, such as finite elopement analysis (FEA). In mathematic terms, geometric modelling is concerned with defining geometric objects using computational geometry, which is often, represented through computer software or rather a geometric modelling kernel. Geometry may be defined with the help of a wire-frame model, surface model, or solid model. Geometric modelling has now become an integral part of any computer-aided design (CAD) system. In this chapter, various geometric modelling approaches, such as wire-frame, surface, and solid modelling will be discussed. Basic computational geometric methods for defining simple entities such as curves, surfaces, and solids are given. Concepts of parametric, variational, history-based, and history-free CAD systems are explained. These topics are discussed in this opening chapter because (a) CAD was the very first computer-aided technologies developed and (b) its related techniques and methods have been pervasive in the other related subjects like computer-aided manufacturing. This chapter only discusses CAD systems from the application point of view; CAD data formats and data exchange issues are covered in the second chapter.

scholarly journals Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Current Level ◽  
Breast Cancer Detection ◽  
Cad Systems ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

scholarly journals A quality enhanced preprocessing method for mammogram ROI extraction

2018 ◽  
Vol 7 (2.25) ◽  
pp. 133
Author(s):  
T R. Thamizhvani ◽  
Bincy Babu ◽  
A Josephin Arockia Dhivya ◽  
R J. Hemalatha ◽  
Josline Elsa Joseph ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Screening Test ◽  
Region Of Interest ◽  
Quality Enhancement ◽  
Cancer Death ◽  
Cad System ◽  
Computer Aided ◽  
Roi Extraction ◽  
Aided Diagnosis

Early detection of breast cancer is necessary because it is considered as one of the most common reason of cancer death among women. Nowadays, the basic screening test for detection of breast cancer is Mammography which con-sists of various artifacts. These artifacts leads to wrong results in detection of breast cancer. Therefore, Computer Aided Diagnosis (CAD) system mainly focus in removal of artifacts and mammogram quality enhancement. By this procedure, exact Region of Interest (ROI) can be obtained. This is a challenging procedure because detection of pecto-ral muscle and cancer region is difficult. Here a comparative study of different preprocessing and enhancement tech-niques are done by testing proposed system on mammogram mini-MIAS database. Result obtained shows that sug-gested system is efficient for CAD system.  

scholarly journals Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

2019 ◽  
Vol 8 (4) ◽  
pp. 12261-12273
Keyword(s):  
Capsule Endoscopy ◽  
Computer Aided Diagnosis ◽  
Current Status ◽  
Human Beings ◽  
Abnormality Detection ◽  
Gi Tract ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

scholarly journals A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

2019 ◽  
pp. 102-112
Author(s):  
J. Juditha Mercina ◽  
J. Madhumathi ◽  
V. Priyanga ◽  
M. Deva Priya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Lung Disease ◽  
Lung Diseases ◽  
Intelligent System ◽  
Lung Disorder ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Lungs play an important role in human respiratory system. There are diseases that affect the functioning of lungs. To analyse lung diseases in the chest region using X-ray based Computer-Aided Diagnosis (CAD) system, it is necessary to determine the lung regions subject to analysis. In this paper, an intelligent system is proposed for lung disease detection. In this paper, Interstitial Lung Disease (ILD) patterns are classified using Convolutional Neural Networks (CNN). The proposed system involves five convolutional layers and three dense layers. The performance of the classification demonstrates the potential of CNN in analysing lung patterns.

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