Brain Degeneration Disease (BD) – is an illness that causes memory loss and cognitive decline. It also causes irreversible brain shrinkage, ultimately resulting in death. The creation of efficient treatments for BD depends on early identification. Utilizing machine learning, a branch of artificial intelligence that enables computer systems to gain knowledge from massive and intricate data sets using a variety of statistical and optimization techniques is one promising strategy for the early detection of BD. Several studies have utilized machine learning techniques to detect BD in its early stages. However, these studies have been criticized for their validity due to factors such as the use of non-pathologically verified datasets from various imaging modalities, differences in pre-processing techniques, feature selection, and class imbalance, making it difficult to make a fair comparison between the results obtained. To address these limitations, a novel model has been developed that involves an initial pre-processing step, followed by attribute selection, and finally classification using association rule mining. This model-based approach shows promise for early diagnosis of BD and can accurately distinguish BD patients from healthy controls. The project utilizes modified versions of convolutional neural networks (CNNs) such as VGG16, AlexNet, and MobileNet, which have been adapted to suit specific research goals and objectives. By using this approach, the authors aim to provide a more reliable and robust model for the early detection of BD, ultimately leading to better outcomes for patients

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