http://bme2.aut.ac.ir/~towhidkhah/SystemIdent/search1/9.pdf

The use of artificial neural networks in decision support in cancer: A systematic review

/pdf/computer-aided-detection-and-diagnosis-in-mammography-4exig6gc0f.pdf

Computer-Aided Detection and Diagnosis in Mammography

In the last few years, several papers have addressed the introduction of contrast-enhanced MR imaging for screening women at high risk for breast cancer. Taking in consideration five prospective studies, on 3,571 screened women with hereditary predisposition to the disease and 9,652 rounds, we found that 168 patients were diagnosed with breast cancer (155 screen-detected, eight interval, and five cancers excluded from analysis) with a detection rate per year of 1.7%. These cancers were small (49% equal to or less than 10 mm in diameter) but aggressive, 82% being invasive and 49% with histologic grade 3; however, only 19% of these invasive cancers were associated with nodal involvement. The pooled sensitivity was 16% for clinical breast examination, 40% for mammography, 43% for ultrasound, and 81% for MR. The positive predictive value (calculated on the basis of the number of invasive diagnostic procedures due to false positives) was 33%, 47%, 18%, and 53%, respectively. Aim of the present article is to present the historical development of MR imaging of breast tumors that made this application theoretically and technically possible, to explain what strategic problems we face in the presence of a hereditary predisposition to the disease, to review the main results of the published studies, and to outline open problems and future perspectives.

Breast MR imaging in women at high-risk of breast cancer. Is something changing in early breast cancer detection?

Early detection of breast cancer is one of the most important factors in determining prognosis for women with malignant tumors. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been shown to be the most sensitive modality for screening high-risk women. Computer-aided diagnosis (CAD) systems have the potential to assist radiologists in the early detection of cancer. A key component of the development of such a CAD system will be the selection of an appropriate classification function responsible for separating malignant and benign lesions. The purpose of this study is to evaluate the effects of variations in temporal feature vectors and kernel functions on the separation of malignant and benign DCE-MRI breast lesions by support vector machines (SVMs). We also propose and demonstrate a classifier visualization and evaluation technique. We show that SVMs provide an effective and flexible framework from which to base CAD techniques for breast MRI, and that the proposed classifier visualization technique has potential as a mechanism for the evaluation of classification solutions.

/pdf/classification-of-dynamic-contrast-enhanced-magnetic-4pive0b15q.pdf

Classification of Dynamic Contrast-Enhanced Magnetic Resonance Breast Lesions by Support Vector Machines

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

The aim of this study was to evaluate the capability of improved artificial neural networks (ANN) and additional novel training methods in distinguishing between benign and malignant breast lesions in contrast-enhanced magnetic resonance-mammography (MRM). A total of 604 histologically proven cases of contrast-enhanced lesions of the female breast at MRI were analyzed. Morphological, dynamic and clinical parameters were collected and stored in a database. The data set was divided into several groups using random or experimental methods [Training & Testing (T&T) algorithm] to train and test different ANNs. An additional novel computer program for input variable selection was applied. Sensitivity and specificity were calculated and compared with a statistical method and an expert radiologist. After optimization of the distribution of cases among the training and testing sets by the T & T algorithm and the reduction of input variables by the Input Selection procedure a highly sophisticated ANN achieved a sensitivity of 93.6% and a specificity of 91.9% in predicting malignancy of lesions within an independent prediction sample set. The best statistical method reached a sensitivity of 90.5% and a specificity of 68.9%. An expert radiologist performed better than the statistical method but worse than the ANN (sensitivity 92.1%, specificity 85.6%). Features extracted out of dynamic contrast-enhanced MRM and additional clinical data can be successfully analyzed by advanced ANNs. The quality of the resulting network strongly depends on the training methods, which are improved by the use of novel training tools. The best results of an improved ANN outperform expert radiologists.

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

M. Thelen

Author Tools

Chat about Author

Papers

Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography