scholarly journals Data Mining Technique to Interpret Lung Nodule for Computer Aided Diagnosis

2011 ◽  
pp. 153-158
Author(s):  
Aswini Kumar Mohanty ◽  
Saroj Kumar Lenka
Keyword(s):  
Data Mining ◽  
Diagnostic Criteria ◽  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Data Mining Technique ◽  
Cad Systems ◽  
Computer Aided ◽  
Using Data ◽  
Aided Diagnosis

Diagnostic decision-making in pulmonary medical imaging has been improved by computer-aided diagnosis (CAD) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Though increasing the accuracy, these CAD systems rarely offer useful descriptions of the suspected nodule or their decision criteria, mainly due to lack of nodule data. In this paper, we present a framework for mapping image features to radiologist-defined diagnostic criteria based on the newly available data). Using data mining, we found promising mappings to clinically relevant, human-interpretable nodule characteristics such as malignancy, margin, spiculation, subtlety, and texture. Bridging the semantic gap between computed image features and radiologist defined diagnostic criteria allows CAD systems to offer not only a second opinion but also decision-support criteria usable by radiologists. Presenting transparent decisions will improve the clinical acceptance of CAD.

scholarly journals A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 393
Author(s):  
Mahsa Mansourian ◽  
Sadaf Khademi ◽  
Hamid Reza Marateb
Keyword(s):  
Data Mining ◽  
Mental Disorders ◽  
Neurological Disorders ◽  
External Validation ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Performance Indices ◽  
Life Years ◽  
Computer Aided ◽  
Aided Diagnosis

The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. Depressive disorders, schizophrenia, bipolar disorder, Alzheimer’s disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). Furthermore, suicide, the 15th leading cause of death worldwide, could be linked to mental disorders. More than 68 computer-aided diagnosis (CAD) methods published in peer-reviewed journals from 2016 to 2021 were analyzed, among which 75% were published in the year 2018 or later. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was adopted to select the relevant studies. In addition to the gold standard, the sample size, neuroimaging techniques or biomarkers, validation frameworks, the classifiers, and the performance indices were analyzed. We further discussed how various performance indices are essential based on the biostatistical and data mining perspective. Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed. We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.

Computer-aided Diagnosis in Lung Nodule Assessment

2008 ◽  
Vol 23 (2) ◽  
pp. 97-104 ◽  
Author(s):  
Jonathan G. Goldin ◽  
Matthew S. Brown ◽  
Iva Petkovska
Keyword(s):  
Lung Nodule ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
D. K. Iakovidis ◽  
T. Goudas ◽  
C. Smailis ◽  
I. Maglogiannis
Keyword(s):  
Kidney Biopsy ◽  
Image Annotation ◽  
Image Force ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Obstructive Nephropathy ◽  
Common Disease ◽  
Annotation Tool ◽  
Computer Aided ◽  
Aided Diagnosis

Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases

2012 ◽  
pp. 86-106
Author(s):  
Issam El Naqa ◽  
Jung Hun Oh ◽  
Yongyi Yang
Keyword(s):  
Relevance Feedback ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Query Process ◽  
Large Databases ◽  
Computer Aided ◽  
High Level ◽  
Aided Diagnosis

With the ever-growing volume of images used in medicine, the capability to retrieve relevant images from large databases is becoming increasingly important. Despite the recent progress made in the field, its applications in Computer-Aided Diagnosis (CAD) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features. Relevance Feedback (RFB) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images, which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy. In this chapter, the authors review some recent advances in RFB technology, and discuss its expanding role in content-based image retrieval from medical archives. They provide working examples, based on their experience, for developing machine-learning methods for RFB in mammography and highlight the potential opportunities in this field for CAD applications and clinical decision-making.

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.

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