The World Health Organization (WHO) claims that neurological disorders provide one of the biggest risks to public health because they can impact up to 1 billion people worldwide and result in 6.8 million fatalities annually.
By utilizing deep learning techniques, this project seeks to overcome these issues by creating a novel method for the early detection of Alzheimer's from brain imaging patterns.
“ Deep Learning- Based Alzheimer's classification from Brain MRI Imaging Patterns: Revolutionizing Neurological Disorders detection“
For this Deep learning project as a part of my internship at FeynnLabs, I have taken a preprocessed dataset from Kaggle (Alzheimer MRI Preprocessed Dataset (128 x 128))
The Data is collected from several websites/hospitals/public repositories. The Dataset consists of Preprocessed MRI (Magnetic Resonance Imaging) Images. All the images are resized into 128 x 128 pixels. The Dataset has four classes of images. The Dataset consists of a total of 6400 MRI images. Class - 1: Mild Demented (896 images) Class - 2: Moderate Demented (64 images) Class - 3: Non Demented (3200 images) Class - 4: Very Mild Demented (2240 images)
The main motive behind sharing this dataset is to design/develop an accurate framework or architecture for the classification of Alzheimer's Disease.
An example of images from the dataset
An example of model prediction
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**Disclaimer: This is a collaborative project that is entirely adapted from an author (Source: Kaggle), although the author (anonymous) has acheived 97% accuracy. With my limited knowledge in Deep learning, utilizing various AI tools, and suggestions from my mentors. My part of the work in this project lies in Image preprocessing and splitting the data into training and testing. I was able to achieve an accuracy of 90% for this project. I want to give the original author credit for their work. The aim of my work towards this project is solely based on my interests in Alzheiemer's categorization and my understanding of Deep-learning algorithms. This project motivated me, developed my interest in DL, and made me confident to carry out my further activities in DL and programming. Trust me, the work was difficult, but it was my desire to learn DL that motivated me to persevere.
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