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

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate proper treatments. Advancements in artificial intelligence and camera technology have allowed us to automate the diagnosis of DR, which can benefit millions of patients indeed. This paper inscribes a novel method for DR diagnosis based on the gray-level intensity and texture features extracted from fundus images using a decision tree-based ensemble learning technique. This study primarily works with the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection (APTOS 2019 BD) dataset. We undertook several steps to curate its contents to make them more suitable for machine learning applications. Our approach incorporates several image processing techniques, two feature extraction techniques, and one feature selection technique, which results in a classification accuracy of 94.20% (margin of error: ±0.32%) and an F-measure of 93.51% (margin of error: ±0.5%). Several other parameters regarding the proposed method’s performance have been presented to manifest its robustness and reliability. Details on each employed technique have been included to make the provided results reproducible. This method can be a valuable tool for mass retinal screening to detect DR, thus drastically reducing the rate of vision loss attributed to it.

Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

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

Early diagnosis and treatment of diabetic retinopathy (DR) can reduce the risk of vision loss. There are five stages of DR consisting of no DR, mild DR, moderate DR, severe DR, and proliferate DR. This paper presents a multitask deep learning model to detect all the five stages of DR more accurately than existing methods. The developed multitask model consists of one classification model and one regression model, each with its own loss function. After training the regression model and the classification model separately, the features extracted by these two models are concatenated and inputted to a multilayer perceptron network to classify the five stages of DR. A modified Squeeze Excitation Densely Connected deep neural network is also developed as part of this multitasking approach. The developed multitask model is applied to the two large Kaggle datasets of APTOS and EyePACS. The results obtained indicate that the developed multitask model achieved a weighted Kappa score of 0.90 and 0.88 for the APTOS and EyePACS datasets, respectively. In addition, the micro and macro average area under the receiver operating characteristic (ROC) curve was found to be 0.96, and 0.93, respectively, which are higher than existing methods for detecting the five stages of DR.

Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy

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. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. 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 extracts different features, fusing them using 1D pooling and cross pooling leads 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 more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.

https://www.mdpi.com/2079-9292/9/6/914/pdf

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

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.

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

Classification of brain tumors is one of the daunting tasks in medical imaging and incorrect decisions during the diagnosis process may lead to increased human fatality. Latest advances in artifici...

Veeranjaneyulu Naralasetti

Papers

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

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

MSENet: Multi-Modal Squeeze-and-Excitation Network for Brain Tumor Severity Prediction