Identifying the most relevant tablet regions in the image detection of counterfeit medicines

2021 ◽  
pp. 114336
Author(s):  
Fábio do Prado Puglia ◽  
Michel José Anzanello ◽  
Jacob Scharcanski ◽  
Juliana de Abreu Fontes ◽  
João Batista Gonçalves de Brito ◽  
...  
Keyword(s):  
Image Detection ◽  
Counterfeit Medicines

Inappropriate thermal image detection system using machine learning

2020 ◽  
Vol 14 (2) ◽  
pp. 102-111
Author(s):  
S. W. Kwon ◽  
I. S. Song ◽  
S. W. Lee ◽  
J. S. Lee ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Thermal Image ◽  
Image Detection

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals Proof-of-Concept of Detection of Counterfeit Medicine through Polymeric Materials Analysis of Plastics Packaging

Polymers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 2185
Author(s):  
Mohammad Salim ◽  
Riyanto Teguh Widodo ◽  
Mohamed Ibrahim Noordin
Keyword(s):  
Polymeric Materials ◽  
Proof Of Concept ◽  
Scanning Calorimetry ◽  
Counterfeit Drug ◽  
Drug Products ◽  
Counterfeit Medicines ◽  
Counterfeit Medicine ◽  
Counterfeit Pharmaceuticals ◽  
Purge Gas ◽  
Sample Set

The detection of counterfeit pharmaceuticals is always a major challenge, but the early detection of counterfeit medicine in a country will reduce the fatal risk among consumers. Technically, fast laboratory testing is vital to develop an effective surveillance and monitoring system of counterfeit medicines. This study proposed the combination of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) and Differential Scanning Calorimetry (DSC) for the quick detection of counterfeit medicines, through the polymer analysis of blister packaging materials. A sample set containing three sets of original and counterfeit medicine was analyzed using ATR-FTIR and DSC, while the spectra from ATR-FTIR were employed as a fingerprint for the polymer characterization. Intending to analyze the polymeric material of each sample, DSC was set at a heating rate of 10 °C min−l and within a temperature range of 0- 400 °C, with nitrogen as a purge gas at a flow rate of 20 ml min−an. The ATR-FTIR spectra revealed the chemical characteristics of the plastic packaging of fake and original medicines. Further analysis of the counterfeit medicine’s packaging with DSC exhibited a distinct difference from the original due to the composition of polymers in the packaging material used. Overall, this study confirmed that the rapid analysis of polymeric materials through ATR-FTIR and comparing DSC thermograms of the plastic in their packaging effectively distinguished counterfeit drug products.

scholarly journals Identification of Skin Lesions by Using Single-Step Multiframe Detector

2021 ◽  
Vol 10 (1) ◽  
pp. 144
Author(s):  
Yu-Ping Hsiao ◽  
Chih-Wei Chiu ◽  
Chih-Wei Lu ◽  
Hong Thai Nguyen ◽  
Yu Sheng Tseng ◽  
...  
Keyword(s):  
Ground Truth ◽  
Skin Lesions ◽  
Single Step ◽  
Single Shot ◽  
Accuracy Rate ◽  
Tissue Slices ◽  
Image Detection ◽  
Sensitivity Rate ◽  
Diagnosis Accuracy ◽  
Artificial Intelligence Algorithm

An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor.

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