Fragile Watermarking of Medical Image for Content Authentication and Security

Currently in the health environment, medical images are a very crucial and important part of the medical information because of the large amount of information and their disposal two-dimensional. Medical images are stored, transmitted and recovered on the network. The images users await efficient solutions to preserve the quality and protect the integrity of images exchanged. In this context, watermarking medical image has been widely recognized as an appropriate technique to enhance the security, authenticity and content verification. Watermarking image may bring elements of complementary research methods of classical cryptography. The objective of this paper is to develop a method to authenticate medical images to grayscale, detect falsified on these image zones and retrieve the original image using a blind fragile watermarking technique. We propose a method based on the discrete wavelet transform (DWT) for the application of content authentication. In our algorithm, the watermark is embedded into the sub-bands detail coefficient. The subbands coefficients are marked by adding a watermark of the same size as three sub-bands and a comparison of embedding a watermark at vertical (LH), horizontal (HL) and diagonal (HH) details. We tested the proposed algorithm after applying some standard types of attacks and more interesting. The results have been analyzed in terms of imperceptibility and fragility. Tests were conducted on the medical images to grayscale and color size 512 × 512

Fragile Watermarking of Medical Image for Content Authentication and Security

Currently in the health environment, medical images are a very crucial and important part of the medical information because of the large amount of information and their disposal two-dimensional. Medical images are stored, transmitted and recovered on the network. The images users await efficient solutions to preserve the quality and protect the integrity of images exchanged. In this context, watermarking medical image has been widely recognized as an appropriate technique to enhance the security, authenticity and content verification. Watermarking image may bring elements of complementary research methods of classical cryptography. The objective of this paper is to develop a method to authenticate medical images to grayscale, detect falsified on these image zones and retrieve the original image using a blind fragile watermarking technique. We propose a method based on the discrete wavelet transform (DWT) for the application of content authentication. In our algorithm, the watermark is embedded into the sub-bands detail coefficient. The subbands coefficients are marked by adding a watermark of the same size as three sub-bands and a comparison of embedding a watermark at vertical (LH), horizontal (HL) and diagonal (HH) details. We tested the proposed algorithm after applying some standard types of attacks and more interesting. The results have been analyzed in terms of imperceptibility and fragility. Tests were conducted on the medical images to grayscale and color size 512 × 512

System segmentation of Lungs in images chest x-ray using the generative adversarial network

One of the most common medical imaging methods is a chest x-ray, as it contributes to the early detection of lung cancer compared to other methods. this work presents the use of a generative adversarial network to perform lung chest x-ray image segmentation. The network is two frameworks neural (generator and discriminator). In our work the generator is trained to generate a mask for the input of a given original image, the discriminator distinguishes between the original mask and the generated mask, the final objective is to generate masks for the input. The model is trained and evaluated, well generalized experimental results of the JSRT dataset reveal that the proposed model can a dice score of 0.9778, which is better than other reported state-of-the-art results

A performant deep learning model for sentiment analysis of climate change

International audienceClimate change is one of the most trend topics of the decade in the world. The recent years were the warmest in 139 years, however identifying deniers and believers of this subject still a very big issue. The challenge is to have an efficient tool to detect deniers in order to deploy the appropriate strategy facing this phenomenon. Moreover, Bidirectional Encoder Representations from Transformers (BERT) pre-trained model has taken Natural Language Processing tasks results so far. In this paper we presented an efficient technological tool based on deep learning model and BERT model for detecting people's opinions on climate change on social media platforms. We used convolutional neural network targeting the public opinions on climate change on Twitter. The results showed that our model outperforms the machine learning approaches: Naive Bays, Support Vector Machine and Logistic Regression. This model is able to analyze people's behavior and detect believers and deniers of this disaster with high accuracy results (98% for believers and 90% for deniers). Our model could be a powerful citizen sensing tool that can be used by governments for monitoring and governance, especially for smart cities

Predictive System of Semiconductor Failures based on Machine Learning Approach

International audienceMaintenance in manufacturing has been developed and researched in the last few decades at a very rapid rate. It's a major step in process control to build a decision tool that detects defects in equipment or processes as quickly as possible to maintain high process efficiencies. However, the high complexity of machines, and the increase in data available in almost all areas, makes research on improving the accuracy of fault detection via data-mining more and more challenging issue in this field. In our paper we present a new predictive model of semiconductor failures, based on machine learning approach, for predictive maintenance in industry 4.0. The framework of our model includes: Dataset and data acquisition, data preprocessing in three phases (over-sampling, data cleaning, and attribute reduction with principal component analysis (PCA) technique and CfsSubsetEval technique), data modeling, evaluation model and implementation model. We used SECOM dataset to develop four different models based on four algorithms (Naive Bayesian, C4.5 Decision tree, Multilayer perceptron (MLP), Support vector machine), according to the five metrics (True Positive rate, False Positive rate, Precision, F-Mesure and Accuracy). We implemented our new predictive model with 91, 95% of accuracy, as a new efficient predictive model of semiconductor failures

Intelligent System Based on GAN Model for Decision Support in Brain Tumor Segmentation

International audienceThe most prevalent malignant brain tumors are gliomas, with a variety of grades, and each grade has a significant impact on a patient's chances of survival. Low-grade gliomas are usually found in the human brain and spinal cord. Low-grade glioma may be accurately diagnosed and detected early, lowering the risk of mortality for patients. In the examination gliomas of low grade, segmentation of MRI images is critical. The result, manual of Segmentation Techniques takes a long time and require a lot of pathology knowledge. in our study, we provide a unique generative adversarial network-based approach for segmenting images of tumors in the brain. The network is a structure between two neurons the generator and the discriminator. The generator is taught to construct an input mask of a take original image, The discriminator can tell the difference between the original and created masks, the end goal is to create masks for the input. The suggested model achieves a dice result of 0.97 in generalized experimental results from the TCGA LGG dataset, with a loss coefficient of 0.030, which is more effective and efficient than the compared approaches

Efficient semi-supervised learning model for limited otolith data using generative adversarial networks

International audienceOtolith shape recognition is one of the relevant tool to ensure the sustainability of maritime resources. It is used to study taxonomy, age estimation and discrimination of stocks of fish species. The most performant otolith image classification models are based on convolutional neural network approaches. To build an efficient system, these models require a large number of labeled images, which is hard to obtain. The lack of data became a big challenge, and a real problem of otolith images classification models, it causes the over-fitting issue, which is the main trouble of deep convolutional neural network based models. In this paper, we present a relevant solution for the insufficiency of data. We propose a new semi-supervised classification model based on generative adversarial network. Our results showed that the model is more efficient and also perform better than convolutional neural network system even with a small training dataset. With this efficiency and performance, we found in addition that the accuracy of the model reached 80% on training set of say, 75 images compared to other models such as a convolutional neural network model which accuracy is limited to 60%