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Brain tumor classification is one of the challenging tasks in the field of medical image analysis. Any faults occurred during brain tumor diagnosis process may result in reduced life span of humans. Automating the process of tumor diagnosis may reduce the number of human errors in diagnosis process. With the recent technological advancements, visual attention for medical image analysis tasks has been widely employed by research community in designing computer-aided diagnosis systems. In this work, we introduce a multi-level attention mechanism for the task of brain tumor recognition. The proposed multi-level attention network (MANet) includes both spatial and cross-channel attention which not only focuses on prioritizing tumor region, but also maintains cross-channel temporal dependencies present in semantic feature sequence obtained from Xception backbone. The performance of proposed approach is evaluated on Figshare and BraTS benchmark datasets. Our experimental studies reveal that, cross-channel attention block along with spatial attention generalizes better and leads to superior performance with fewer model parameters. Our proposed MANet achieved a highest accuracy of 96.51% on Figshare and 94.91% on BraTS’2018 datasets and outperformed several existing models for the tumor recognition task.

Diabetic retinopathy (DR) is one of the main causes of loss of vision and blindness in humans across the world. DR is usually found in patients suffering from diabetes for a long period. Automation of DR diagnosis rescues many people from going blind by identifying the disease at the early stages. In this work, we introduce a robust model for DR severity level prediction by leveraging features extracted from pre-trained models to represent DR images. The activation filter values from multiple convolution blocks of VGG-16 are extracted and aggregated using pooling and fusion methods. The aggregation module produces a compact, informative, and discriminative representation of the retinal images by removing noisy and redundant features using pooling and fusion approaches. These feature representations are fed to the proposed DNN architecture to identify the severity level of DR. On the benchmark Kaggle APTOS 2019 contest dataset, our proposed method sets a new state-of-the-art result with an accuracy of 84.31% and an AUC 97. Experimental studies reveal that the proposed model exhibits superior performance compared with the existing models, especially in the case of severe and proliferate stage DR images.

Diabetic Retinopathy (DR) is a micro vascular complication caused by long-term diabetes mellitus. Unidentified diabetic retinopathy leads to permanent blindness. Early identification of this disease requires frequent complex diagnostic procedure which is expensive and time consuming. In this article, we propose a composite deep neural network architecture with gated-attention mechanism for automated diagnosis of diabetic retinopathy. The feature descriptors obtained from multiple pre-trained deep Convolutional Neural Networks (CNNs) are used to represent color fundus retinal images. Spatial pooling methods are introduced to get the reduced versions of these representations without loosing much information. The proposed composite DNN learns independently from each of these reduced representations through different channels and contributes to improving the model generalization. In addition, model also includes gated attention blocks which allows the model to emphasize more on lesion portions of the retinal images while reduced attention to the non-lesion regions. Our experiments on APTOS-2019 Kaggle blindness detection challenge reveal that, the proposed approach leads to improved performance when compared to the existing best models. Our empirical studies also reveal that, the proposed approach leads to more generalised predictions with multi-modal representations when compared to those of uni-modal representations. The proposed composite deep neural network model recorded an accuracy of 82.54% ( $$\uparrow $$ 2%), and a Kappa score of 79 ( $$\uparrow 9$$ points) for diabetic retinopathy severity level prediction.

Brain tumor recognition is a challenging task, and accurate diagnosis increases the chance of patient survival. In this article, we propose a two-channel deep neural network architecture for tumor classification that is more generalizable. Initially, local feature representations are extracted from convolution blocks of InceptionResNetV2 and Xception networks and are vectorized using proposed pooling-based techniques. An attention mechanism is proposed that allows more focus on tumor regions and less focus on non-tumor regions which eventually helps to differentiate the type of tumor present in the images. The proposed two-channel model allows joint training of two sets of tumor image representations in an end-to-end manner to achieve good generalization. Empirical studies on Figshare and BraT’S2018, benchmark datasets, reveal that our approach is superior in terms of generalization and simple in terms of number of layers compared to the existing complex models that follow fine-tuning of deep CNN models. Avoiding too much preprocessing and augmentation techniques, the proposed model sets new state-of-the-art scores on both the brain tumor datasets.

Diabetic retinopathy (DR) is a severe eye disease which can lead to permanent blindness. Identifying DR in early stages by using computer-aided diagnosis (CAD) systems can help the ophthalmologists to give proper treatment rationally, there by preventing many people from going blind. Due to intra-class variations and imbalanced data distribution, it is highly difficult to design a CAD system for DR severity diagnosis with greater generalizability. In this article, we propose a multi-stage deep learning pipeline, lesion-aware attention with neural support vector machine, for diabetic retinopathy diagnosis. Proposed pipeline consists of a pre-trained convolution base for learning retinal image spatial representations, lesion-aware attention for weighting lesion specific features, convolution autoencoder for learning latent attention representations and a neural support vector machine for discrimination. Convolutional autoencoder and neural support vector machine are jointly trained in end-to-end fashion to obtain category based lesion specific latent attention features by complementing each other in re-constructor and discriminator paths. Proposed approach is validated using two benchmark retinal scan image datasets, Kaggle APTOS 2019 and ISBI 2018 IDRiD, for DR type and severity grade classification tasks. Our experimental studies expose that using lesion-aware attention along with the joint training of autoencoder and neural support vector machine boosted the performance of models used for DR diagnosis, thereby outperforming existing works presented in the literature for DR severity grading. Proposed model achieved the highest accuracy of 90.45%, 84.31% on APTOS dataset and an accuracy of 79.85%, 63.24% on IDRiD dataset for DR type and severity grade classification tasks, respectively.

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.