Medical image classification for Alzheimer's using a deep learning approach.

Medical image categorization is essential for a variety of medical assessments and education functions. The purpose of medical image classification is to organize medical images into useful categories for the purpose of illness diagnosis or study, making it one of the most pressing issues in the field of image recognition. On the other hand, traditional methods have plateaued in their effectiveness. Additionally, a substantial amount of time and energy is required when employing them to extract and choose categorization features. Alzheimer's disease is one of the most frequent sources of dementia in elderly patients. Metabolic diseases affect a huge population worldwide, and henceforth, there is a vast scope of applying machine learning to find treatments to these diseases. As a relatively new machine learning technique, deep neural networks have shown great promise for a variety of categorization problems. In this research, a model for diagnosing and tracking the development of Alzheimer's disease that is both accurate and easy to understand has been developed. By following the developed procedure, medical professionals may make deliberations with solid justification. Early diagnosis utilizing these machine learning algorithms has the potential to minimize mortality rates associated with Alzheimer's disease. This research work has developed a convolutional neural network using a shallow convolution layer to identify Alzheimer's disease in medical image patches. The total accuracy of proposed classifications is around 98%, which is greater than the accuracy of the most popular existing approaches. [ABSTRACT FROM AUTHOR]