Publications Scientifiques

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Author: search.filters.author.Bekkouche, AbdelghaniSubject: search.filters.subject.Deep learning

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    Aerial forest smoke’s fire detection using enhanced YOLOv5
    (Springer, 2023) Cherifi, Dalila; Bekkour, Belkacem; Benmalek, Assala; Bayou, Meroua; Mechti, Ines; Bekkouche, Abdelghani; Amine, Chaima; Halak, Ahmed
    Forest fires around the world are the main cause of devastating millions of forest hectares, destroying several infrastructures and unfortunately causing many human casualties among both fire fighting crews and civilians that might be accidentally surrounded by the fire. The early detection of more than 58,950 forest fires and the real-time fire perception are two key factors that allow the firefighting crews to act accordingly in order to prevent the fire from achieving unmanageable proportions [1]. Forest fire detection is such a challenging problem for the current world. Traditional methodologies depend on a set of expensive hardware and sensors that might be not accurate due to some environment parameters and weather fluctuations. This paper proposes an accurate intelligent deep learning-based YOLOv5 model to detect forest fires from a given aerial images
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    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, Halak
    Emerging 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 training