scholarly journals An Effective Texture Features Based Mammogram Mass Detection System

2018 ◽  
Vol 7 (3.12) ◽  
pp. 601 ◽  
Author(s):  
K Rajendra Prasad ◽  
T Suneetha Rani ◽  
Suleman Basha
Keyword(s):  
Detection System ◽  
Texture Features ◽  
Regions Of Interest ◽  
Mass Detection ◽  
Medical Field ◽  
Final Point ◽  
Computer Aided ◽  
Long Time ◽  
Recognition Systems ◽  
Quality Features

The identification of Mammogram is a very complicated application in Bio-medical field, it has complicated tissues. Nowadays breast cancer test, Bio-medical field often miss approximately 10% - 30% of tumors because of the ambiguous margins of lesions and visual weakness ensuing from long-time identification. For these reasons, numerous computer-aided recognition systems have been residential to aid Bio-medical in detecting mammographic lesions which may point out the existence of breast cancerthis revision presents a repeated Computer detection system that uses limited and isolated quality features for mammographic mass recognition. And system segments some adaptive square regions of interest (ROIs) for apprehensive areas. This revise also proposes two tricky feature withdrawal methods based on co-occurrence environment and visual compactness alteration to illustrate restricted quality uniqueness and the isolated photometric allocation of each ROI. As a final point, this revision uses stepwise linear discriminate examination to grade typical regions by selecting and evaluating the entity presentation of each feature. Consequences demonstrate that the projected system achievesacceptable recognition concert.

BOUNDARY SEGMENTATION AND DETECTION OF DIABETIC RETINOPATHY (DR) IN FUNDUS IMAGE

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
R. Samad ◽  
M. S. F. Nasarudin ◽  
M. Mustafa ◽  
D. Pebrianti ◽  
N. R. H. Abdullah
Keyword(s):  
Diabetic Retinopathy ◽  
Medical Image ◽  
Processing Time ◽  
Detection System ◽  
Computer Aided Detection ◽  
Fundus Image ◽  
Medical Field ◽  
Computer Aided ◽  
Automatic Detection System ◽  
Number Of Iterations

Recently, the automatic detection system or Computer-Aided Detection (CAD) is widely developed in the medical field to screen or diagnose the medical image. This paper presents the boundary segmentation and detection of Diabetic Retinopathy (DR) in fundus image. The proposed method uses Fuzzy C-Means for clustering and detect the boundary of the DR object. The number of cluster used in this work is 3 and the average number of iterations is 28.The DR region is successfully detected by FCM and the average processing time is 1.235s.  

Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms

2013 ◽  
Vol 40 (4) ◽  
pp. 041902 ◽  
Author(s):  
Guido van Schie ◽  
Matthew G. Wallis ◽  
Karin Leifland ◽  
Mats Danielsson ◽  
Nico Karssemeijer
Keyword(s):  
Detection System ◽  
Digital Breast Tomosynthesis ◽  
Computer Aided Detection ◽  
Mass Detection ◽  
Breast Tomosynthesis ◽  
Computer Aided

scholarly journals Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rohit Kundu ◽  
Hritam Basak ◽  
Pawan Kumar Singh ◽  
Ali Ahmadian ◽  
Massimiliano Ferrara ◽  
...  
Keyword(s):  
Ct Scan ◽  
Detection System ◽  
Network Models ◽  
Test Cases ◽  
Gompertz Function ◽  
Neural Network Models ◽  
Source Codes ◽  
Ensemble Strategy ◽  
The World ◽  
Long Time

AbstractCOVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

Computer-Aided Nodule Detection System

2015 ◽  
Vol 22 (4) ◽  
pp. 475-480 ◽  
Author(s):  
Feng Li ◽  
Roger Engelmann ◽  
Samuel G. Armato ◽  
Heber MacMahon
Keyword(s):  
Detection System ◽  
Nodule Detection ◽  
Computer Aided

Accuracy of Segmentation of a Commercial Computer-aided Detection System for Mammography

Radiology ◽  
2005 ◽  
Vol 235 (2) ◽  
pp. 385-390 ◽  
Author(s):  
Jay A. Baker ◽  
Eric L. Rosen ◽  
Michele M. Crockett ◽  
Joseph Y. Lo
Keyword(s):  
Detection System ◽  
Computer Aided Detection ◽  
Computer Aided
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