A predictive Deep Learning Model trained on MRI images of Brain for Tumor Detection. This application aims to provide prior diagnosis for the existence of a tumor in a given brain MRI image.
| Tumor Image | No Tumor Image |
|---|---|
 |
 |
model = Sequential()
model.add(Conv2D(64, (3,3), input_shape = X_train.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2))) # Pooling
model.add(Conv2D(64, (3,3), input_shape = X_train.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2))) # Pooling
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss = 'binary_crossentropy',
optimizer = 'adam',
metrics = ['accuracy'])
| Layer (type) | Output Shape | Param |
|---|---|---|
| conv2d (Conv2D) | (None, 148, 148, 64) | 640 |
| activation (Activation) | (None, 148, 148, 64) | 0 |
| max_pooling2d (MaxPooling2D) | (None, 74, 74, 64) | 0 |
| conv2d_1 (Conv2D) | (None, 72, 72, 64) | 36928 |
| activation_1 (Activation) | (None, 72, 72, 64) | 0 |
| max_pooling2d_1 (MaxPooling 2D) | (None, 36, 36, 64) | 0 |
| flatten (Flatten) | (None, 82944) | 0 |
| dense (Dense) | (None, 64) | 5308480 |
| dense_1 (Dense) | (None, 1) | 65 |
| activation_2 (Activation) | (None, 1) | 0 |
Here is the Link for the dataset. The data folder consists of Yes and No folders which has MRI images that are labeled as Tumor and No-Tumor respectively.
1. tensorflow==2.2.1
2. numpy==1.18.5
3. opencv-python==4.2.0.34
4. pandas==1.0.4
5. scikit-learn==0.23.1