Communications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11
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Item Study and implementation of u-net encoder-decoder neural network for brain tumors segmentation(Springer, 2023) Cherifi, Dalila; Bekkouche, Abdelghani; Bayou, Meroua; Benmalek, Assala; Mechti, Ines; Bekkour, Belkacem; Amine, Chaima; Ahmed, HalakEmerging advanced technologies have seen a revolution of applications into medical field, in all its aspects and sides, this has helped healthcare practitioners and empowered them in achieving accurate diagnosis and treatment, specifically with the evolution of computer Aided Diagnosis systems which use image processing techniques, Computer vision,and deep learning applied on different medical images in order to diagnose the image, or sections of the image with particular diseases or illnesses. Medical images of multiples organs or parts of the body (Liver, brain, kidney, skin, etc..) can today be visualized thanks to the advanced medical imaging techniques that exists in the market (MRI, CT, etc…) these technologies uses high energy in order to acquire high quality images but high energy can harm human cells, this is why we us low energy and with this used we get slightly low quality medical images, and here technology intervenes where we can use preprocessing techniques in order to increase image resolution prior to perform diagnosis either by doctor or CAD system. We present in this paper a computer aided diagnosis system that provides an automated brain tissue segmentation applied on 3D MRI images with its four different modalities (T1, T1C, T2, T2 weighted) of BRatS 2020 challenge dataset, by implementing a U-Net like deep neural network which provides information about classification of brain tissue into healthy tissue, Edema, Enhancing tumour, Non enhancing tumour. The model achieved an accuracy of 99.01% and dice coefficient of 47.95% after 35 epochs of trainingItem Edge detection of MRI brain images based on segmentation and classification using support vector machines and neural networks pattern recognition(Springer, 2023) Iourzikene, Zouhir; Benazzouz, Djamel; Gougam, FawziBrain tumor (brain cancer) is a mass of abnormal cells that grow in the brain in an uncontrolled way. Brain CT and brain MRI are the most frequently performed examinations. The objective of this paper is to develop a method for the classification of brain MRI images of healthy cases and tumor cases. MRI brain database is obtained by preprocessing, segmentation, feature extraction. Feature extraction based on support vector machines (clustering) is used in this research. The objective of this method is to create several vectors and each vector contains a number of features of each image, so that we can make the classification by these featuresItem Strategy of detecting abnormal behaviors by fuzzy logic(IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedThis work falls within the framework of the video surveillance research axis. This work falls within the scope of video surveillance. It involves a link between automatic processing and problems related to video surveillance. The job is to analyze video streams coming from a network of surveillance cameras, deployed in an area of interest in order to detect abnormal behavior. Our approach in this article relies on the new application and the use of fuzzy logic in the case of division and fusion of the crowd. The detection of these behaviors will increase the speed of response of the security services in order to perform accurate analysis and detection of events in real timeItem Towards a generic Multi-agent approach for medical image segmentation(Springer, 2017) Bennai, Mohamed Tahar; Guessoum, Zahia; Mazouzi, Smaine; Cormier, Stéphane; Mezghiche, MohamedItem Detection method without crowd behavior modeling by fuzzy logic(2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, Mohamed