- Abstract: In this manuscript, we present a deep learning based approach for detection and classification of medical conditions such as classification of breast cancer and grading of diabetic retinopathy and macular edema. The performance of a convolutional neural network is dependent on the architecture of the network, amount of training data and data pre-processing. Transfer learning is oft utilized in deep learning so as to counter the limited availability of high quality annotated data. Hence, we create an ensemble of pre-trained classifiers by making use of models with different topologies and data normalization schemes. In general, the variance associated with an ensemble of classifiers is lower compared to a single classifier and thus generalizes better on unseen data. An F1 score based model pruning technique was utilized for deciding the optimal number of classifiers in the ensemble. The proposed technique was tested on two separate biomedical image challenges, namely the (1) classification of breast cancer from histology images [BACH-2018] and (2) grading of diabetic retinopathy and macular edema from fundus images [IDRiD-2018]. On the histology data, our technique was adjudged jointly as the top performing algorithm while for the task of diabetic retinopathy grading, the technique was declared as the 4th best performing algorithm.
- Keywords: convolutional neural networks, transfer learning, breast cancer, diabetic retinopathy, macular edema, ensemble