A New CAD System for Early Diagnosis of Detected Lung Nodules

Author(s):  
Ayman El-Baz ◽  
Georgy Gimel'farb ◽  
Robert Falk ◽  
Mohamed Abo El-Ghar
Author(s):  
Ammar Chaudhry ◽  
Ammar Chaudhry ◽  
William H. Moore

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.


Author(s):  
Ali Mahmoud ◽  
Yaser ElNakieb ◽  
Ahmed Shalaby ◽  
Ahmed Soliman ◽  
Fatma Taher ◽  
...  

2020 ◽  
Vol 32 (03) ◽  
pp. 2050017
Author(s):  
Ayat Karrar ◽  
Mai S. Mabrouk ◽  
Manal AbdEl Wahed

Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the lowest survival rates compared to other cancers. CT scans can reveal dense masses of different shapes and sizes; in the lungs, these are called lung nodules. This study applied a computer-aided diagnosis (CAD) system to detect candidate nodules — and diagnose it either solitary or juxtapleural — with equivalent diameters, ranging from 7.78[Formula: see text]mm to 22.48[Formula: see text]mm in a 2D CT slice. Pre-processing and segmentation is a very important step to segment and enhance the CT image. A segmentation and enhancement algorithm is achieved using bilateral filtering, Thresholding the gray-level transformation function, Bounding box and maximum intensity projection. Border artifacts are removed by clearing the lung border, erosion, dilation and superimposing. Feature extraction is done by extracting 20 gray-level co-occurrence matrix features from four directions: [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] and one distance of separation ([Formula: see text] pixel). In the classification step, two classifiers are proposed to classify two types of nodules based on their locations: as juxtapleural or solitary nodules. The two classifiers are a deep learning convolutional neural network (CNN) and the K-nearest neighbor (KNN) algorithm. Random oversampling and 10-fold cross-validation are used to improve the results. In our CAD system, the highest accuracy and sensitivity rates achieved by the CNN were 96% and 95%, respectively, for solitary nodule detection. The highest accuracy and sensitivity rates achieved by the KNN model were 93.8% and 96.7%, respectively, and K was set to 1 to detect juxtapleural nodules.


2011 ◽  
Vol 58-60 ◽  
pp. 1378-1383
Author(s):  
Ming Zhi Qu ◽  
Gui Rong Weng

Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.


2020 ◽  
Author(s):  
Reem Haweel ◽  
Ahmed Shalaby ◽  
Ali Mahmoud ◽  
Noha Seada ◽  
Said Ghoniemy ◽  
...  
Keyword(s):  

CHEST Journal ◽  
2011 ◽  
Vol 140 (4) ◽  
pp. 655A
Author(s):  
Muralidhar Kondapaneni ◽  
Matthew Nitzken ◽  
Eric Bogaert ◽  
Georgy Gimel'farb ◽  
Robert Falk ◽  
...  

Author(s):  
Islam Reda ◽  
Ahmed Shalaby ◽  
Mohamed Abou El-Ghar ◽  
Fahmi Khalifa ◽  
Mohammed Elmogy ◽  
...  

2019 ◽  
Vol 485 (5) ◽  
pp. 558-563
Author(s):  
V. F. Kravchenko ◽  
V. I. Ponomaryov ◽  
V. I. Pustovoit ◽  
E. Rendon-Gonzalez

A new computer-aided detection (CAD) system for lung nodule detection and selection in computed tomography scans is substantiated and implemented. The method consists of the following stages: preprocessing based on threshold and morphological filtration, the formation of suspicious regions of interest using a priori information, the detection of lung nodules by applying the fractal dimension transformation, the computation of informative texture features for identified lung nodules, and their classification by applying the SVM and AdaBoost algorithms. A physical interpretation of the proposed CAD system is given, and its block diagram is constructed. The simulation results based on the proposed CAD method demonstrate advantages of the new approach in terms of standard criteria, such as sensitivity and the false-positive rate.


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