scholarly journals Smart Spotting of Pulmonary TB Cavities Using CT Images

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
V. Ezhil Swanly ◽  
L. Selvam ◽  
P. Mohan Kumar ◽  
J. Arokia Renjith ◽  
M. Arunachalam ◽  
...  
Keyword(s):  
False Positive Rate ◽  
Mean Shift ◽  
Gradient Vector Flow ◽  
Computer Aided Detection ◽  
Cad System ◽  
High Infection ◽  
Positive Rate ◽  
Shift Model ◽  
Automatic Technique ◽  
Inverse Coefficient

One third of the world’s population is thought to have been infected with mycobacterium tuberculosis (TB) with new infection occurring at a rate of about one per second. TB typically attacks the lungs. Indication of cavities in upper lobes of lungs shows the high infection. Traditionally, it has been detected manually by physicians. But the automatic technique proposed in this paper focuses on accurate detection of disease by computed tomography (CT) using computer-aided detection (CAD) system. The various steps of the detection process include the following: (i) image preprocessing, which is done by techniques such as resizing, masking, and Gaussian smoothening, (ii) image egmentation that is implemented by using mean-shift model and gradient vector flow (GVF) model, (iii) feature extraction that can be achieved by Gradient inverse coefficient of variation and circularity measure, and (iv) classification using Bayesian classifier. Experimental results show that its perfection of detecting cavities is very accurate in low false positive rate (FPR).

scholarly journals The detection of lung cancer using MTANN (Massive Training Artificial Neural Network) based soft tissue technique

2020 ◽  
Author(s):  
Kishore Rajagopalan ◽  
Suresh babu
Keyword(s):  
Lung Cancer ◽  
Soft Tissue ◽  
False Positive ◽  
False Positive Rate ◽  
Computer Aided Detection ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Positive Rate ◽  
Image Set

Abstract Background An existing computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer. Method In the proposed computer aided detection (CAD) system, this issue has been resolved by creating MTANN based soft tissue technique from the lung segmented x-ray image. X-ray images are downloaded using JSRT(Japanese society of radiological technology) image set. JSRT image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represent subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists. Result An existing computer aided detection (CAD) scheme attained 66.42% (93/140) sensitivity having 2.5 false positives (FPs) per image. Utilizing MTANN based soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) at one false positive rate). Conclusion In particular, proposed computer aided detection (CAD) system using soft tissue technique determine sensitivity in support of subtle nodules (14/42=33.33%) is statistically higher than CAD (13/42=30.95%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition.

scholarly journals Classification of lung nodules using CT images based on texture features and fractal dimension transformation

2019 ◽  
Vol 485 (5) ◽  
pp. 558-563
Author(s):  
V. F. Kravchenko ◽  
V. I. Ponomaryov ◽  
V. I. Pustovoit ◽  
E. Rendon-Gonzalez
Keyword(s):  
Fractal Dimension ◽  
Block Diagram ◽  
False Positive Rate ◽  
A Priori ◽  
Lung Nodule ◽  
Texture Features ◽  
Lung Nodules ◽  
Cad System ◽  
Positive Rate ◽  
Priori Information

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.

scholarly journals Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

2021 ◽  
Vol 11 (23) ◽  
pp. 11398
Author(s):  
Salvador Castro-Tapia ◽  
Celina Lizeth Castañeda-Miranda ◽  
Carlos Alberto Olvera-Olvera ◽  
Héctor A. Guerrero-Osuna ◽  
José Manuel Ortiz-Rodriguez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Imaging Techniques ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Fine Tuning ◽  
Cad System ◽  
Leading Role ◽  
Operational Characteristics ◽  
Positive Rate

Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

A CAD SCHEME FOR EARLY LUNG CANCER DETECTION IN CHEST RADIOGRAPHY

2017 ◽  
Vol 29 (05) ◽  
pp. 1750037
Author(s):  
Jorge Juan Suárez-Cuenca ◽  
Amara Tilve ◽  
Gonzalo Ferro ◽  
Ricardo López ◽  
Javier Quiles ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Chest Radiography ◽  
False Positive Rate ◽  
Clinical Environment ◽  
Free Response ◽  
Integration Model ◽  
Cad System ◽  
Positive Rate ◽  
Performance Results ◽  
Lung Cancer Detection

The purpose of this work is to describe a chest radiography computer-aided diagnostic (CAD) scheme designed to analyze the chest radiographs performed in the framework of the Galician (Spain) Health Service (GHS), including the radiographs that are not reported by the radiologists. The final goal of this CAD system is its integration in the GHS daily clinical environment, with a feasible RIS-PACS-CAD and EHR-CAD integration model. The database of the study included 55 chest radiographies with 64 nodules/lung cancer. This database was used to develop and test the CAD system in our research laboratory. Free-Response Receiver Operating Characteristic (FROC) curves were employed to evaluate the performance of the CAD system. An independent database was employed to evaluate the performance of the CAD system by external radiologists. After the application of a linear classifier, our CAD system achieved a sensitivity of 70% with a false positive rate between 4 and 6 per image depending on the testing database. When compared with other commercial systems, our CAD scheme achieved similar performance results. Therefore, our CAD scheme could be utilized to help radiologists in the detection of lung nodules in chest radiography, and therefore, it can be integrated in the clinical practice.

scholarly journals Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Panpan Wu ◽  
Xuanchao Sun ◽  
Ziping Zhao ◽  
Haishuai Wang ◽  
Shirui Pan ◽  
...  
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Lung Nodule ◽  
Residual Network ◽  
Cad System ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Positive Rate ◽  
And Migration ◽  
Nodule Classification

The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.

An Innovative Approach for Detection of Armoured Vehicle in Airborne Thermal Imagery Using Morphological Processing and Texture Feature Extraction

2017 ◽  
Vol 26 (2) ◽  
pp. 359-370
Author(s):  
Asheesh Kumar Gautam ◽  
Lokesh K. Sinha ◽  
Mahendra R. Bhutiyani
Keyword(s):  
Gabor Filter ◽  
False Positive Rate ◽  
Mean Shift ◽  
Texture Feature ◽  
True Positive Rate ◽  
Detection Algorithm ◽  
Thermal Image ◽  
Morphological Processing ◽  
Thermal Imagery ◽  
Positive Rate

AbstractAutomatic detection of a vehicle in an airborne thermal imagery is a challenging research topic in computer vision, especially the detection of military tanks in the field. Various methodologies for detection in forward-looking infrared imagery, which has higher spatial resolution, has been discussed by a number of researchers in literature. The algorithm we developed in the present study detects tanks not only in higher resolution but in lower resolution imagery as well. Detection algorithm is initiated by the segmentation of thermal image using mean shift, which provides possible targets present in the field other than the background. To reduce clutter and uneven illumination in a thermal image, a pre-processing morphological algorithm based on top-hat filtering has been implemented. After convolution of image window with Gabor filter banks, we extracted the energy feature of each image generated after convolution. The energy vector of such a target and the neighbouring background window has been calculated, and the similarity between the target and background using distance-measuring method has been measured. The minimum distance is used as the threshold to decide the target. A comparative study has been carried out between tanks and various targets/objects that appear similar to tanks in a thermal image. This validates our target detection algorithm. The false-positive rate and true-positive rate have been calculated for performance evaluation. Overall, this algorithm shows promising results for tank detection using single-band thermal imagery.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

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