AI has revolutionized medical imaging, diagnosis, and therapy through the utilization of machine learning (ML) and deep learning (DL) techniques. We have gathered and thoroughly analyzed a diverse range of research publications. Our primary aim is to raise awareness regarding how AI can significantly enhance the accuracy, efficiency, and patient outcomes of medical imaging. This can prove invaluable in planning personalized treatments and image-guided therapies.
By utilizing AI in image/video analysis, we can gain insightful knowledge by fusing imaging data with other patient-specific data, resulting in more thorough and individualized healthcare.
Alzheimer's disease is a condition where neurons within the brain stop functioning, lose connection with other neurons, and die. It's the most common cause of dementia, a loss of brain function that can adversely impact memory, thinking, language, judgment, and behavior. Alzheimer's is irreversible and progressive.
In this use case, we have created dataframes for the training and validation data, which contain file paths and corresponding category labels including 'MildDemented', 'ModerateDemented', 'NonDemented', and 'VeryMildDemented'. Furthermore, we have created data generators using the Image DataGenerator.These generators are used for loading and augmenting image data during training and validation. We then evaluated performance on multiple deep learning neural networks.
During the training process, the model is initialized with weights from ImageNet, a large-scale image database. These pre-trained weights capture general features from a wide variety of images. In the final step, we have evaluated the trained model on the validation and test datasets, calculating loss and accuracy metrics.
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