Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. Major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. In order to extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extract different features, fusing them using 1-D pooling, and cross pooling lead to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that, DNN with dropout at input layer converges faster when trained using blended features, than compared to the same model trained using uni-modal deep features.
Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.
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