# Brain tumor classification using CNN

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## Model Details

### Model Description

```python
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)](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri) and
[Brain MRI Images for Brain Tumor Detection](https://www.kaggle.com/datasets/navoneel/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
{"language": ["de", "en"], "license": "apache-2.0", "library_name": "keras", "metrics": ["accuracy", "roc_auc"], "pipeline_tag": "image-classification"}
keras image-classification de en license:apache-2.0 has_space region:us
TAGS
#keras #image-classification #de #en #license-apache-2.0 #has_space #region-us 

# Brain tumor classification using CNN


## Model Details

### Model Description



### 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
# Brain tumor classification using CNN ## Model Details ### Model Description ### 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
TAGS
#keras #image-classification #de #en #license-apache-2.0 #has_space #region-us 
 # Brain tumor classification using CNN ## Model Details ### Model Description ### 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