A deep learning model to classify healthy and malaria infected person from their blood smear images.
Achieved accuracy is 96.8125 %
5 models/techniques are used in this Jupyter Notebook. Their accuracies are as follows:
- Basic CNN (without Data Augmentation) -- 95.9525 %
- Transfer Learning (VGG-19) (without Data Augmentation) -- 93.2750 %
- Transfer Learning (fine-tuned VGG-19) (without Data Augmentation) -- 96.6000 %
- Transfer Learning (fine-tuned VGG-19) (with Data Augmentation) -- 96.7625 %
- Ensembling (3) and (4) -- 96.8125 %
- Shallow network (with just 3 CNN layers) overfits the data very early (in just about 4 epochs), but provides a good accuracy too.
- Using deeper architecture results in higher accuracy only when we fine-tune the network by training middle layers too.
- On using data augmentation, we get a little higher accuracy. But it is very beneficial as the validation loss is effectively less compared to non-augmented model. So, it predicts with higher confidence.
- Ensembling the model makes benefits from both the models. Thus instead of getting the mean accuracy of the two, we get slightly higher accuracy than both the individual models.