Use of deep learning in health care

AI has revolutionized medical imaging, diagnosis, andtherapy through the utilization of machine learning (ML) and deep learning (DL)techniques. We have gathered and thoroughly analyzed a diverse range of researchpublications. Our primary aim is to raise awareness regarding how AI cansignificantly enhance the accuracy, efficiency, and patient outcomes of medical imaging. This can prove invaluable in planning personalized treatments andimage-guided therapies.

By utilizing AI in image/video analysis, we can gaininsightful 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 thebrain stop functioning, lose connection with other neurons, and die. It's themost common cause of dementia, a loss of brain function that can adversely impact memory, thinking, language, judgment, and behavior. Alzheimer's isirreversible and progressive.

In this use case, wehave 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 ImageDataGenerator. 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.

Tags: image processing, alzheimer , deep learning, health care, medical diagnosis

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