scholarly journals Semantic and Content-Based Medical Image Retrieval with Proven Pathology for Lung Cancer Diagnosis

2021 ◽  
Vol 15 ◽  
pp. 221-227
Author(s):  
Preeti Aggarwal ◽  
H. K. Sardana ◽  
Renu Vig
Keyword(s):  
Lung Cancer ◽  
Image Retrieval ◽  
Medical Image ◽  
Semantic Information ◽  
Lung Nodule ◽  
Ground Truth ◽  
Image Database ◽  
Cad Systems ◽  
Lung Cancer Diagnosis ◽  
Aided Diagnosis

In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. Due to lack of ground truth and semantic information, lung CAD systems are not progressing in the manner these are supposed to. In this study, we have explored Lung Image Database Consortium (LIDC) database containing annotated pulmonary computed tomography (CT) scans, and we have used semantic and content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. We evaluated the method by various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems while tested on PGIMER data. Also a little knowledge of biopsy confirmed cases can also assist the physician’s as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies

scholarly journals Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

2013 ◽  
Vol 2013 ◽  
pp. 1-46 ◽  
Author(s):  
Ayman El-Baz ◽  
Garth M. Beache ◽  
Georgy Gimel'farb ◽  
Kenji Suzuki ◽  
Kazunori Okada ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Clinical Importance ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Lung Cancer Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Processing Steps

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effectivecomputer-aided diagnosis(CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

scholarly journals An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1457
Author(s):  
Muazzam Maqsood ◽  
Sadaf Yasmin ◽  
Irfan Mehmood ◽  
Maryam Bukhari ◽  
Mucheol Kim
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Ct Scan ◽  
Cancer Diagnosis ◽  
Lung Nodule ◽  
Search Space ◽  
Lung Nodules ◽  
Suggested Model ◽  
Lung Cancer Diagnosis ◽  
Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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 SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH

2021 ◽  
Vol 9 (10) ◽  
pp. 1294-1300
Author(s):  
Aigli Korfiati ◽  
◽  
Giorgos Livanos ◽  
Christos Konstandinou ◽  
Sophia Georgiou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Medical Image ◽  
Skin Lesions ◽  
Learning Approaches ◽  
Early Screening ◽  
Clinical Images ◽  
Cad Systems ◽  
Learning Classifier ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

scholarly journals Partitioning Medical Image Databases for Content-based Queries on a Grid

2005 ◽  
Vol 44 (02) ◽  
pp. 154-160 ◽  
Author(s):  
V. Breton ◽  
I. E. Magnin ◽  
J. Montagnat
Keyword(s):  
Theoretical Model ◽  
Image Retrieval ◽  
Medical Image ◽  
Computation Time ◽  
Image Database ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Medical Databases ◽  
Grid Nodes ◽  
The Impact

Summary Objectives: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. Methods: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. Results: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. Conclusions: Grids are promising for content-based image retrieval in medical databases.

Sign in / Sign up

Export Citation Format

Share Document