click here to download project base paper of Alzheimer’s disease
click here to download project abstract
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– ABSTRACT
Image processing employs a range of algorithms to extract meaningful information from these images. Each algorithm operates uniquely, leading to variations in efficiency. To obtain the requisite information, several image segmentation algorithms are applied, and the accuracy of their results is validated using ground truth data. hence This comparison between the segmented images and ground truth data serves as a metric for assessing the efficiency of each algorithm.
The research zeroes in on U-net techniques, a prominent method in the realm of medical image segmentation. The study evaluates the outcomes achieved through these algorithms, establishing a foundation for the development of an algorithm specifically tailored for detecting it. Focusing on Alzheimer’s disease, we base the entire process on 2-D brain images and subsequently validate the resulting outputs, which are also 2-D images.
In summary, the research delves into the intricate process of Alzheimer’s disease detection through the analysis of 2-D brain images, employing advanced image segmentation techniques and leveraging the power of U-net algorithms to achieve accurate and reliable results in the quest for improved diagnostic methodologies.
