Browsing by Author "Ali, Soltani"

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    Towards transformers application in computer vision
    (university of eloued جامعة الوادي, 2023-06-07) Anis, Hannanou; Ali, Soltani
    Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. The field of medical image analysis has been particularly interested in leveraging the advancements made by Transformers, as opposed to the traditional Convolutional Neural Networks (CNNs). Transformers have proven to be effective in various medical image processing applications, including classification, registration, segmentation, detection, and diagnosis. The purpose of this memoir is to raise awareness about the potential applications of Transformers in medical image processing. we provide firstly an overview of the fundamental concepts of artificial intelligence and its relevance to computer vision, with a specific focus on how Transformers and other essential components contribute to these advancements. Second, we conduct a comprehensive review of different Transformer architectures tailored for medical image applications. We explore their specific applications and discuss the challenges associated with using visual Transformers in this domain. Within this dissertation we delve into the significant differences between CNNs and Transformers, with emphasising the proposed classification model enhancement image (brain MRI) by comparing the results with CNN model.Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. The field of medical image analysis has been particularly interested in leveraging the advancements made by Transformers, as opposed to the traditional Convolutional Neural Networks (CNNs). Transformers have proven to be effective in various medical image processing applications, including classification, registration, segmentation, detection, and diagnosis. The purpose of this memoir is to raise awareness about the potential applications of Transformers in medical image processing. we provide firstly an overview of the fundamental concepts of artificial intelligence and its relevance to computer vision, with a specific focus on how Transformers and other essential components contribute to these advancements. Second, we conduct a comprehensive review of different Transformer architectures tailored for medical image applications. We explore their specific applications and discuss the challenges associated with using visual Transformers in this domain. Within this dissertation we delve into the significant differences between CNNs and Transformers, with emphasising the proposed classification model enhancement image (brain MRI) by comparing the results with CNN model.Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. The field of medical image analysis has been particularly interested in leveraging the advancements made by Transformers, as opposed to the traditional Convolutional Neural Networks (CNNs). Transformers have proven to be effective in various medical image processing applications, including classification, registration, segmentation, detection, and diagnosis. The purpose of this memoir is to raise awareness about the potential applications of Transformers in medical image processing. we provide firstly an overview of the fundamental concepts of artificial intelligence and its relevance to computer vision, with a specific focus on how Transformers and other essential components contribute to these advancements. Second, we conduct a comprehensive review of different Transformer architectures tailored for medical image applications. We explore their specific applications and discuss the challenges associated with using visual Transformers in this domain. Within this dissertation we delve into the significant differences between CNNs and Transformers, with emphasising the proposed classification model enhancement image (brain MRI) by comparing the results with CNN model.