Alzheimers and Brain Tumor Detection Using Deep Learning

In several industries, such as manufacturing, construction, and the Accurate detection of brain tumors and Alzheimer’s disease is essential for effective treatment and disease management. With the rapid progress in deep learning technologies, the field of medical imaging—particularly the interpretation of brain scans—has seen remarkable improvements. This research focuses on utilizing two well-established convolutional neural network (CNN) architectures, VGG-19 and ResNet-50, for brain tumor classification, while employing a standard CNN model for detecting Alzheimer’s disease. VGG-19, characterized by its consistent and deep structure comprising 19 layers, is particularly effective in extracting complex features due to its sequential convolutional layers. This makes it well-suited for identifying subtle patterns in MRI images of the brain. In contrast, ResNet-50 incorporates residual connections within its 50-layer design, allowing the model to mitigate issues like vanishing gradients and improving learning efficiency by enabling the network to focus on residual mappings. This study compares both models to evaluate their accuracy, resilience, and computational efficiency in detecting brain abnormalities. Moreover, the research examines each model's ability to generalize across various datasets and tumor types, aiming to provide insights into their clinical applicability. The results may contribute to refining current diagnostic techniques, promoting earlier detection, and assisting in the development of advanced tools for accurate brain tumor diagnosis and treatment planning. Integrating these models into healthcare systems could improve diagnostic accuracy and enhance patient care outcomes. Alzheimer’s disease, the most prevalent form of dementia, leads to progressive memory loss, impaired thinking, and behavioral changes. Its symptoms typically worsen over time, eventually hindering the ability to perform everyday tasks. Dementia is a broad term describing a range of symptoms caused by cognitive decline, with Alzheimer’s accounting for 60% to 80% of all cases. Vascular dementia, often following a stroke, is the second most common type, though several other reversible conditions—like thyroid imbalances and vitamin deficiencies—can produce similar symptoms.In this study, we use publicly available datasets for Alzheimer’s detection. The system employs deep learning models, particularly CNN and ResNet, to analyze the data. The outcomes demonstrate the model's ability to accurately classify the disease into categories such as mild, moderate, very moderate, and dementia, based on performance metrics like prediction accuracy.