Optimizing-CNN-MRI-Alzheimer-Stages-Detection

academic project that aims to teach how to optimize cnn networks and understand the impact of various training parameters on model performance :)

OPTIMIZED MODEL:

Structure	Img size	Samples	Augmentation	Normalization	Accelerator	Transfer learning	Dropout	Batch size	Epochs
MobileNet	224x224	12200	YES	YES	GPU	YES	NO	32	16

dataset: https://www.kaggle.com/datasets/uraninjo/augmented-alzheimer-mri-dataset/data
(im using non augmented version because of project requirements)
models raports are on 'project' branch (in polish)

>Assigned instructions for the project:

1. Prepare a dataset for image classification (binary or multiclass). ✅

2. Train a model using the ResNet50 architecture from scratch (without transfer learning) on CPU. ✅

• The obtained results (training time and accuracy) will serve as a baseline for subsequent experiments.

3. To optimize training speed, apply:

a. GPU acceleration – prepare a comparative report showing training times with and without GPU. ✅
b. Transfer learning – prepare a comparative report with and without transfer learning; we are particularly interested in reaching a certain accuracy level, e.g., 80%. ✅

4. To optimize accuracy, apply:

a. Normalization – prepare a comparative report with and without data normalization. ✅
b. Data augmentation – prepare a comparative report with and without augmentation, including details of the transformations used. ✅
c. Dropout – prepare a comparative report with and without applying dropout. ✅
d. Data extension – prepare a comparative report with and without adding a new batch of data. ✅
e. Different input sizes (e.g., 96x96, 160x160, 224x224) – prepare a comparative report for each size. ✅
f. Different batch sizes (e.g., 32, 64, 128) – prepare a comparative report for each size. ✅
g. Different network architectures (e.g., VGG16, ResNet101, InceptionV3, MobileNet) – prepare a comparative report for each architecture (at least 4). ✅

todo

• change dataset to regular not augmented and train again :( ✅
• check variables names ✅

todo in the future

• !! get rid of code redundancy !! (make functions that automatically change images input sizes, batch sizes, models etc)

• https://www.geeksforgeeks.org/image-classification-using-resnet/
• https://miroslawmamczur.pl/jak-dzialaja-konwolucyjne-sieci-neuronowe-cnn/
• https://www.youtube.com/watch?v=OxYGhiUDJYQ