Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages-no-DR, mild, moderate, severe and proliferative DR-as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.

https://www.mdpi.com/1424-8220/21/11/3704/pdf

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

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.

Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification

Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based on magnetic resonance imaging. For computer-aided diagnosis methods to assist radiologists in the proper detection of brain tumors, medical imagery must be detected, segmented, and classified. Manual brain tumor detection is a monotonous and error-prone procedure for radiologists; hence, it is very important to implement an automated method. As a result, the precise brain tumor detection and classification method is presented. Materials and Methods: The proposed method has five steps. In the first step, a linear contrast stretching is used to determine the edges in the source image. In the second step, a custom 17-layered deep neural network architecture is developed for the segmentation of brain tumors. In the third step, a modified MobileNetV2 architecture is used for feature extraction and is trained using transfer learning. In the fourth step, an entropy-based controlled method was used along with a multiclass support vector machine (M-SVM) for the best features selection. In the final step, M-SVM is used for brain tumor classification, which identifies the meningioma, glioma and pituitary images. Results: The proposed method was demonstrated on BraTS 2018 and Figshare datasets. Experimental study shows that the proposed brain tumor detection and classification method outperforms other methods both visually and quantitatively, obtaining an accuracy of 97.47% and 98.92%, respectively. Finally, we adopt the eXplainable Artificial Intelligence (XAI) method to explain the result. Conclusions: Our proposed approach for brain tumor detection and classification has outperformed prior methods. These findings demonstrate that the proposed approach obtained higher performance in terms of both visually and enhanced quantitative evaluation with improved accuracy.

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Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

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.

Multi-level attention network: application to brain tumor classification

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.

Joint training of two-channel deep neural network for brain tumor classification

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.

Nagur Shareef Shaik

Papers

Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification

Joint training of two-channel deep neural network for brain tumor classification

Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction

Hinge attention network: A joint model for diabetic retinopathy severity grading

Visual attention based composite dense neural network for facial expression recognition