A brief description of what this project does and who it's for
Alzheimer's disease (AD) is an irreversible and degenerative brain illness that gradually robs sufferers of their memory and cognitive functions, leaving them unable to perform even the most fundamental activities. AD is a prevalent disease, especially among the elderly. AD is the most prevalent form of dementia. Recent statistics indicate that more than 55 million people worldwide have dementia, and 10 million new cases are recorded annually. In 2022, it is estimated that 6.5 million Americans aged 65 and older will have Alzheimer's disease. If no medical improvements are made to prevent, halt, or cure Alzheimer's disease, this number might reach 13.8 million by 2060.
Included in a general categorization of Alzheimer's dementia are the following:
•Mild Cognitive Impairment: This disorder affects many people as they age, causing them to forget things, but in some cases, it can progress to dementia.
•Moderate Dementia: Individuals with moderate dementia exhibit cognitive impairments that occasionally interfere with their daily lives. Symptoms include amnesia, hesitancy, personality changes, disorientation, and trouble doing regular tasks.
•Moderate Dementia: The patient needs extra care and assistance as the complexity of their everyday life increases. The symptoms mirror a somewhat severe form of dementia. For some, even brushing their hair may require further assistance. Additionally, they may exhibit significant personality changes, such as abrupt paranoia or anger. Sleep disturbances are also possible.
• Severe Dementia: Symptoms may worsen at this stage. Some people who lack the ability to communicate may require round-the-clock care. Even simple things, such as sitting in a chair and holding one's head up straight, may be difficult for someone who has lost bladder control.
To delay the abnormal deterioration of the brain, reduce the cost of medical care, and improve therapy, researchers are investigating the early identification of this disease. Recent failures in Alzheimer's disease research suggest that early identification and intervention may be crucial to the efficacy of treatment.
In this repository, AD detection has been performed by utilizing a dataset from Kaggle and AlexNet, which may result in superior performance.
Dataset:
In this repository, AD detection has been performed by utilizing a dataset from Kaggle and AlexNet, which may result in superior performance.
Imbalanced classification entails building predictive models on classification datasets with a high degree of class imbalance. Working with unbalanced datasets presents the difficulty that most machine learning algorithms will overlook, and so perform poorly on, the minority class, despite the fact that performance on the minority class is often the most essential. Oversampling the minority class is one method for dealing with unbalanced datasets. The most basic method is duplicating instances from the minority class, even if these examples offer no new information to the model. Instead, new instances may be created by combining old ones. The Synthetic Minority Oversampling Technique, or SMOTE in brief, is a kind of data augmentation for the minority class.
Link on GitHub