Computer-aided shape analysis and classification of weld defects in industrial radiography based invariant attributes and neural networks

2005 ◽  
Author(s):  
N. Nacereddine ◽  
M. Tridi
Keyword(s):  
Neural Networks ◽  
Shape Analysis ◽  
Weld Defects ◽  
Industrial Radiography ◽  
Computer Aided

scholarly journals Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5551
Author(s):  
Chan-Il Kim ◽  
Seok-Min Hwang ◽  
Eun-Bin Park ◽  
Chang-Hee Won ◽  
Jong-Ha Lee
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Diagnostic Algorithm ◽  
The United States ◽  
Classification Model ◽  
Benign Tumors ◽  
Computer Aided

Malignant melanoma accounts for about 1–3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.

scholarly journals An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

2021 ◽  
Vol 8 ◽  
Author(s):  
Tommaso Banzato ◽  
Marek Wodzinski ◽  
Federico Tauceri ◽  
Chiara Donà ◽  
Filippo Scavazza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pleural Effusion ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Computer Aided Detection ◽  
Radiographic Findings ◽  
Computer Aided ◽  
Operating Characteristic Curve

An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training. The presence of several radiographic findings was recorded. Consequenly, the radiographic findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly. Multi-label convolutional neural networks (CNNs) were used to develop the CAD algorithm, and the performance of two different CNN architectures, ResNet 50 and Inception V3, was compared. Both architectures had an area under the receiver operating characteristic curve (AUC) above 0.9 for alveolar pattern, bronchial pattern and pleural effusion, an AUC above 0.8 for no findings and pneumothorax, and an AUC above 0.7 for cardiomegaly. The AUC for mass was low (above 0.5) for both architectures. No significant differences were evident in the diagnostic accuracy of either architecture.

MFL signals and artificial neural networks applied to detection and classification of pipe weld defects

2006 ◽  
Vol 39 (8) ◽  
pp. 661-667 ◽  
Author(s):  
A.A. Carvalho ◽  
J.M.A. Rebello ◽  
L.V.S. Sagrilo ◽  
C.S. Camerini ◽  
I.V.J. Miranda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Weld Defects ◽  
Artificial Neural
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