Experimental performance records

Params

Pre-trained weights: imagenet - resnet50
Input shape: (224, 224, 3)
Encoder trainable: False
Batch size: 32
Epochs: 533

Model

Input Image (224, 224, 3) → resnet50 (224, 224, 32) → Result (4)

Total params: 49,725,436
Trainable params: 40,912,012
Non-trainable params: 8,813,424

Dataset

• Medial Images for Gastric Cancer Diagnosis (Korean)

Preprocessing

• Preprocessors

1. Remove malformed images from entire dataset.
2. Fill missing polygons from label data using CVAT.
3. Exports its ROI, binary masks and transformed original images.
4. Select pixel from binary masks.

Compile options

Loss

• categorical_crossentropy

Metrics

• categorical_accuracy

Optimizer

• Adam

Results

Evaluation