- is malignent images,
- is benign images,
- inpainted images(after hair removal)
- anatom_site_general_challenge pi chart,
- count of age approx,
- count of diagnosis and many more in the notebooks.
model.add(VGG19(include_top=False, weights='imagenet', input_shape= inputShape))
model.add(Flatten())
model.add(Dense(32))
model.add(LeakyReLU(0.001))
model.add(Dense(16))
model.add(LeakyReLU(0.001))
model.add(Dense(1, activation='sigmoid'))
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
resnet101 (Model) (None, 7, 7, 2048) 42658176
_________________________________________________________________
global_average_pooling2d (Gl (None, 2048) 0
_________________________________________________________________
dense (Dense) (None, 1) 2049
=================================================================
Total params: 42,660,225
Trainable params: 42,554,881
Non-trainable params: 105,344
_________________________________________________________________
- ResNet101 training inside
resnet101-with-focal-loss-and-img-aug.ipynb
- VGG16 training is in
baseline-submission-keras-vgg16
- Image Analysis is in
cancer-detection-analysis.ipynb
- Tabular data analysis is in
eda-w-plotly-and-stacking-on-tabular-data-0-685.ipynb
- woking on the image analysis more
- woking on bettering the model performance.