Image based diagnostic aid system for interstitial lung diseases

Tolouee, Azar; Moghaddam, Hamid Abrishami; Forouzanfar, Mohamad; Gity, Masumeh; Garnavi, Rahil

doi:10.1016/j.eswa.2010.12.133

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…Aasia Rehman, Dr. Majid Zaman, and Dr. Muheet Ahmed Butt reported a range of medical imaging methods in their survey [21] Panfang Hua's [22] region growth approach is unreliable in high attenuation patterns like ILD, but it performs well in the presence of noise and can correctly distinguish areas with the same properties. Azar Tolouee devised a threshold method that chooses the best threshold to distinguish the lung region from the backdrop [23]. It was discovered to be quite complex for ILD and missing several typical lung features.The fuzzy method is used for segmentation by M.Gomathi and P.Thangaraj [24], which takes less time between iterations to obtain the overall optimal solution but is unable to separate images corrupted by noise, outliers, and other artefacts.…”

Section: Related Workmentioning

confidence: 99%

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Yadlapalli¹,

Bhavana

2022

SCPE

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Medical image processing involves using and examining 3D human body images, which are most frequently acquired through a computed tomography scanner, to diagnose disorders. Medical image process- ing helps radiologists, engineers, and clinicians better comprehend the anatomy of specific patients or groups of patients. Due to recent advancements in deep learn ing techniques, the study of medical image analysis is now a quickly expanding area of research. Interstitial Lung Disease is a chronic lung disease that worsens with time. This condition cannot be completely treated when the lungs have been damaged. Early detection, on the other hand, aids in the control of the disease. It causes lung scarring as a result. The first methodology characterizes lung tissue utilizing first order statistics, grey live occurrence, run length matrices, and fractal analysis. It was suggested by Uppaluri et al in one instance. In the pre-processing step, patients' CT scans are presented using various color map models for better understanding of data-set. and also for determining the patients final Force Vital Capacity and Confidence values using a Pytorch model with leaky relu activation function. These variables can be used to determine whether a person has a disease. Segmentation is a crucial stage in employing a computer assisted diagnosis system to estimate interstitial lung disease. Accurate segmentation of aberrant lung is essential for a trustworthy computer-aided illness diagnosis. Using separate training, validation, and test sets, we proposed an efficient deep learning model using Unet architecture and Densenet121 to segment lungs with Interstitial Lung Disease. The proposed segmentation model distinguishes the exact lung region from the ct slice background. To train and evaluate the algo rithm, 176 sparsely annotated Computed Tomography scans were utilized. The training was completed in a supervised and end to end manner. Contrary to current approaches, the suggested method yields accurate segmentation results without the requirement for re-initialization. We were able to achieve an accuracy of 92.59 percent after training the proposed model with Nvidia's CUDA GPU.

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Section: Related Workmentioning

confidence: 99%

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Yadlapalli¹,

Bhavana

2022

SCPE

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“…To overcome this effect, wavelet frames 20 and discrete wavelet frame (DWF) 21 were introduced by Depeursinge et al which provide continuous response, flexibility, and translation invariance but resolution is poor. Hence to cope up with these effects, quincunx wavelet frames (QWF) 22 is introduced, which utilizes the desirable properties of the isotropic polyharmonic B-spline wavelets combined with the quincunx subsampling that provides translation, rotation, and scale invariance. Even though, QWF specially preserves isotropy with direct and easy interpretation of the sub-bands in wavelets, performance was poor due to unavailability of directional information.…”

Section: Introductionmentioning

confidence: 99%

Interstitial lung disease detection using template matching combined sparse coding and blended multi class support vector machine

Sulochana

Blessy²

2022

Proc Inst Mech Eng H

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Interstitial lung disease (ILD), representing a collection of disorders, is considered to be the deadliest one, which increases the mortality rate of humans. In this paper, an automated scheme for detection and classification of ILD patterns is presented, which eliminates low inter-class feature variation and high intra-class feature variation in patterns, caused by translation and illumination effects. A novel and efficient feature extraction method named Template-Matching Combined Sparse Coding (TMCSC) is proposed, which extracts features invariant to translation and illumination effects, from defined regions of interest (ROI) within lung parenchyma. The translated image patch is compared with all possible templates of the image using template matching process. The corresponding sparse matrix for the set of translated image patches and their nearest template is obtained by minimizing the objective function of the similarity matrix of translated image patch and the template. A novel Blended-Multi Class Support Vector Machine (B-MCSVM) is designed for tackling high-intra class feature variation problems, which provides improved classification accuracy. Region of interests (ROIs) of five lung tissue patterns (healthy, emphysema, ground glass, micronodule, and fibrosis) selected from an internal multimedia database that contains high-resolution computed tomography (HRCT) image series are identified and utilized in this work. Performance of the proposed scheme outperforms most of the state-of-art multi-class classification algorithms.

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“…Finally, for classification tasks, we observed the use of classifiers such as k-nearest neighbor (Filipczuk et al, 2013;Gedik and Atasoy, 2013;Gopinath and Shanthi, 2013;He et al, 2011;Muramatsu et al, 2013;Nava et al, 2014;Odeh et al, 2006;Osman et al, 2009;Raja et al, 2010;Verikas et al, 2006), artificial neural networks (Barhoumi et al, 2007;Geetha et al, 2008;Jasmine et al, 2009;López et al, 2008;Raja et al, 2007;Streba et al, 2012;Verma, 2009;Wu et al, 2006), Bayesian classifiers (Ampeliotis et al, 2007;Bhooshan et al, 2011;Garnavi et al, 2012;Gruszauskas et al, 2008Gruszauskas et al, , 2009Retter et al, 2013;Tolouee et al, 2011) techniques based on linear discriminant analysis (Lee et al, 2009;Muramatsu et al, 2013;Tanner et al, 2006) and logistic regression models (Shen et al, 2007;Tanner et al, 2006).…”

Section: Tasks For Computer-aided Diagnosis Systemsmentioning

confidence: 99%

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

Gonçalves

Delamaro

Nunes

2014

Rev. Bras. Eng. Bioméd.

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Introduction: One of the challenges in developing Computer-Aided Diagnosis (CAD) systems is their accurate and comprehensive assessment. This paper presents the conduction and results of a systematic review (SR) that aims to verify the state of the art regarding the assessment of CAD systems. This survey provides a general analysis of the current status of the design, development and assessment of such systems and includes discussions on the most used metrics and approaches that could be utilized to obtain more objective evaluation methods. Methods: The SR was conducted using the scientifi c databases, ACM Digital Library, IEEE Xplore Digital Library, ScienceDirect and Web of Science. Inclusion and exclusion criteria were defi ned and applied to each retrieved work to select those of interest. From 156 studies retrieved, 100 studies were included. Results: There is a number of abnormalities that have been used for the development of CAD systems. Images from computed tomographies and mammographies are the most encountered types of medical images. Additionally, a number of studies used public databases for CAD evaluations. The main evaluation metrics and methods applied to CAD systems include sensitivity, accuracy, specifi city and receiver operating characteristic (ROC) analyses. In the assessed CAD systems that used the segmentation method, 30.0% applied the overlap measure. Discussion: There remain several topics to explore for the assessment of CAD schemes. While some evaluation metrics are traditionally used, they require a prior knowledge of case characteristics to test CAD systems. We were not able to identify articles that use software testing to evaluate CAD systems. Thus, we realize that there is a gap between CAD assessments and traditional practices of software engineering. However, the scope of this research is limited to scientifi c and academic works and excludes commercial interests. Finally, we discuss potential research studies within this scope to create a more objective and effi cient evaluation of CAD systems.

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Image based diagnostic aid system for interstitial lung diseases

Cited by 10 publications

References 31 publications

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Interstitial lung disease detection using template matching combined sparse coding and blended multi class support vector machine

A systematic review on the evaluation and characteristics of computer-aided diagnosis systems

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