Healthcare

Automatic Diabetic Retinopathy Detection using pretrained models

1.Use predict.py to test an image.

2.link for ResNet model trained with 30 epochs when only classifier is trained https://drive.google.com/file/d/14gKzlEUiipWXxfL94CQNftFOmb3t31Gf/view?usp=sharing

3.link for ResNet model trained with 30 epochs when following layers are added: https://drive.google.com/file/d/1gvKICsx8S5BEWLbek_gKJAWdbQELwTED/view?usp=sharing

Fine tune the resnet 50

#image_input = Input(shape=(224, 224, 3)) model = ResNet50(weights='imagenet',include_top=False) model.summary() last_layer = model.output

add a global spatial average pooling layer

x = GlobalAveragePooling2D()(last_layer)

add fully-connected & dropout layers

x = Dense(512, activation='relu',name='fc-1')(x) x = Dropout(0.5)(x) x = Dense(256, activation='relu',name='fc-2')(x) x = Dropout(0.5)(x)

a softmax layer for 4 classes

out = Dense(num_classes, activation='softmax',name='output_layer')(x)

this is the model we will train

custom_resnet_model2 = Model(inputs=model.input, outputs=out) custom_resnet_model2.summary()