Browsing by Author "Tisha, Esrat Jahan"

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    AI-Driven Bone Fracture Detection:
    (Daffodil International University, 2024-07-24) Emon, Mir Md. Asif Jahan; Tisha, Esrat Jahan
    This study, titled "AI-Driven Bone Fracture Detection: Leveraging Image Processing and Machine Learning on X-ray Images," embarks on enhancing the accuracy and efficiency of diagnosing bone fractures using advanced AI techniques. Utilizing a dataset of X-ray images augmented with metadata on patient demographics and clinical details, several deep learning models, including VGG16, MobileNetV2, InceptionV3, ResNet50, and hybrid combinations, were trained and validated. These models demonstrate substantial promise in identifying and classifying bone fractures with varying degrees of precision. This study gets a high accuracy of 89% in MobileNetV2 while using fully raw data. The research highlights the superior performance of MobileNetV2 and hybrid models, which combine the strengths of multiple neural network architectures to optimize fracture detection. By integrating these AI models into clinical settings, the study aims to alleviate the workload on radiologists, expedite diagnostic processes, and potentially enhance patient care by offering rapid and accurate fracture evaluations. Moreover, the study explores the ethical dimensions of AI deployment in medical diagnostics, focusing on data privacy, bias mitigation, and system transparency. As the integration of AI in healthcare progresses, this research paves the way for future explorations into expanding the models' capabilities to other medical imaging modalities and developing real-time diagnostic tools. This work not only advances the field of medical AI but also sets a benchmark for future research aimed at refining AI-driven diagnostics in healthcare.
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    AI-Driven Bone Fracture Detection: Leveraging Image Processing and Machine Learning on X-ray Images
    (Daffodil International University, 2024-07-14) Emon, Mir Md. Asif Jahan; Tisha, Esrat Jahan
    This study, titled "AI-Driven Bone Fracture Detection: Leveraging Image Processing and Machine Learning on X-ray Images," embarks on enhancing the accuracy and efficiency of diagnosing bone fractures using advanced AI techniques. Utilizing a dataset of X-ray images augmented with metadata on patient demographics and clinical details, several deep learning models, including VGG16, MobileNetV2, InceptionV3, ResNet50, and hybrid combinations, were trained and validated. These models demonstrate substantial promise in identifying and classifying bone fractures with varying degrees of precision. This study gets a high accuracy of 89% in MobileNetV2 while using fully raw data. The research highlights the superior performance of MobileNetV2 and hybrid models, which combine the strengths of multiple neural network architectures to optimize fracture detection. By integrating these AI models into clinical settings, the study aims to alleviate the workload on radiologists, expedite diagnostic processes, and potentially enhance patient care by offering rapid and accurate fracture evaluations. Moreover, the study explores the ethical dimensions of AI deployment in medical diagnostics, focusing on data privacy, bias mitigation, and system transparency. As the integration of AI in healthcare progresses, this research paves the way for future explorations into expanding the models' capabilities to other medical imaging modalities and developing real-time diagnostic tools. This work not only advances the field of medical AI but also sets a benchmark for future research aimed at refining AI-driven diagnostics in healthcare.