Browsing by Author "Fatiha Tamma Hanaa Zeghidi"

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    Deep Learning Approaches for Stroke Detection Using CNN and Transfer Learning Techniques
    (2023-10) Fatiha Tamma Hanaa Zeghidi
    to enhance the diagnosis of stroke disease through medical image classification. Leveraging the power of neural networks, we conducted a comparative study involving popular transfer learning techniques such as VGG16, VGG19, and ResNet-50. While these complex architectures demonstrated their potential, we also recognized the pitfalls of deep models, leading us to introduce a simpler Convolutional Neural Network (CNN) model. Through rigorous evaluation, our proposed CNN model achieved an exceptional accuracy of 99.60%, underscoring the efficacy of a streamlined approach. Our findings emphasize the balance between sophisticated methodologies and pragmatic solutions, showcasing how AI can significantly impact medical diagnoses. As a practical application, we developed an intuitive application that enables users to classify medical images, bridging the gap between AI advancements and real-world medical practices. This project contributes to the advancement of AI in healthcare, showcasing the potential for accurate and efficient stroke disease diagnosis.