Neural Network Image Classification

Objective: Predicting whether patient has the Alzheimer's disease (binary output: 1 - has Alzheimer's, 0 - doesn't have Alzheimer's)

Data Used: link

Source: Kaggle

Device/s used: Google TPUv2

Alzheimer MRI Preprocessed Dataset (128 x 128)

The Data is collected from several websites/hospitals/public repositories. The Dataset is 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 is consists of total 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).

For the purpose of the project, data will be divided into 2 specific classes:

1. Not demented brain MRI pictures

2. Demented brain MRI pictures

Models that are going to be used and tested:

EfficientNet is a relatively young neural network architecture which has a unique feature - "compound scaling". EfficientNet systematically scales models dimensions, such as width, depth, and resolution which boosts efficiency without compromising accuracy. That's why I decided to test this model.

1. EfficientNetV2B0 (Baseline model).

2. EfficientNetV2S (Small) - A larger EfficientNet model with higher number of parameters and ability to catch patterns of the data.