Brain tumor classification using CNN
Model Details
Model Description
model.summary()
Out:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
rescaling (Rescaling) (None, 200, 200, 1) 0
conv2d (Conv2D) (None, 200, 200, 16) 160
max_pooling2d (MaxPooling2D (None, 100, 100, 16) 0
)
conv2d_1 (Conv2D) (None, 100, 100, 32) 4640
max_pooling2d_1 (MaxPooling (None, 50, 50, 32) 0
2D)
conv2d_2 (Conv2D) (None, 50, 50, 64) 18496
max_pooling2d_2 (MaxPooling (None, 25, 25, 64) 0
2D)
flatten (Flatten) (None, 40000) 0
dense (Dense) (None, 128) 5120128
dense_1 (Dense) (None, 64) 8256
dense_2 (Dense) (None, 128) 8320
dense_3 (Dense) (None, 64) 8256
dense_4 (Dense) (None, 32) 2080
dense_5 (Dense) (None, 96) 3168
dense_6 (Dense) (None, 96) 9312
dense_7 (Dense) (None, 128) 12416
dense_8 (Dense) (None, 1) 129
=================================================================
Total params: 5,195,361
Trainable params: 5,195,361
Non-trainable params: 0
_________________________________________________________________
Dataset
The dataset is composed of Brain Tumor Classification (MRI) and Brain MRI Images for Brain Tumor Detection
Using image data augmentation we get 199.632 files belonging to 2 classes.
train-test-split: 80/20
Training (159705 files):
- Using 143735 files for training
- Using 15970 files for validation
Test/Validation:
- 39927 files
Training
Coming Soon
Validation
Coming Soon
Hardware
Lenovo Thinkpad P14s
- CPU (# Cores/Threads): AMD Ryzen 7 PRO 5850U (8/16)
- RAM: 32 GB
Software
DataSpell 2023.1.2
- Python 3.10.9
- Tensorflow 2.12.0
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