Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

Ashraf, Muhammad; Hussain, Muhammad; Habib, Zulfiqar

doi:10.2174/1573405615666190219102427

Cited by 3 publications

(1 citation statement)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pre-processing of DR images [21,22] having different steps such as color space, Spatial normalization, Region of Interest (ROI) extraction, Illumination correction, contrast enhancement and Vessels extraction etc. The entire process of preprocessing of images shown in the Figure 3.…”

Section: Preprocessing Of Dr Imagesmentioning

confidence: 99%

A Contemporary Method on Feature Selection and Classification Using Multi-Model Deep Learning Technique for Identifying Diabetic Retinopathy

Meenakshi¹,

Thailambal²

2021

Advances in Parallel Computing Technologies and Applications

View full text Add to dashboard Cite

Diabetic retinopathy is one of the leading reason for preventable blindness in the world. 10–18 % of diabetic people having diabetic retinopathy. The feature selection and classification is a vital task to find the seriousness of the diabetic retinopathy. The different researchers introduced different techniques to extract the features and classification of diabetic retinopathy images. The deep learning is one of the essential methods to extract the features. Most of the previous techniques are extracted information’s with the help of texture and extracted the whole image feature data. Some feature missed and thereby the accuracy is significantly less. Hence a proposed new technique called FRCNN (Fast Region-based Convolution Network) and Nearest Neighbour (NN) algorithm used to extract the features and classifications. The proposed method yields better accuracy (96%), sensitivity (98%) and specificity (97%) compared to the previous methods. The implementations Messidor Dataset is used for training and testing

show abstract

Section: Preprocessing Of Dr Imagesmentioning

confidence: 99%

A Contemporary Method on Feature Selection and Classification Using Multi-Model Deep Learning Technique for Identifying Diabetic Retinopathy

Meenakshi¹,

Thailambal²

2021

Advances in Parallel Computing Technologies and Applications

View full text Add to dashboard Cite

show abstract

Modified capsule network for diabetic retinopathy detection and classification using fundus images

Aswini

Sivarani²

2023

IFS

View full text Add to dashboard Cite

Diabetic retinopathy becomes an increasingly popular cause of vision loss in diabetic patients. Deep learning has recently received attention as one of the most popular methods for boosting performance in a range of sectors, including medical image analysis and classification. The proposed system comprises three steps; they are image preprocessing, image segmentation, and classification. In preprocessing, the image will be resized, denoising the image and enhancing the contrast of the image which is used for further processing. The lesion region of diabetic retinopathy fundus image is segmented by using Feature Fusion-based U-Net architecture. A blood vessel of a retinal image is extracted by using the spatial fuzzy c means clustering (SFCM) algorithm. Finally, the diabetic retinopathy images are classified using a modified capsule network. The convolution and primary capsule layers collect features from fundus images, while the class capsule and softmax layers decide whether the image belongs to a certain class. Using the Messidor dataset, the proposed system’s network efficiency is evaluated in terms of four performance indicators. The modified contrast limited adaptive histogram equalization technique enhanced the Peak Signal to Noise Ratio (PSNR), mean square error, and Structural Similarity Index Measure (SSIM) have average values of 36.18, 6.15, and 0.95, respectively. After enhancing the image, segmentation is performed to segment the vessel and lesion region. The segmentation accuracy is measured for the proposed segmentation algorithm by using two metrics namely intersection over union (IoU) and Dice similarity coefficient. Then modified capsule network is constructed for classifying the stages of diabetic retinopathy. The experimental result shows that the proposed modified capsule network got 98.57% of classification accuracy.

show abstract

Deep Red Lesion Classification for Early Screening of Diabetic Retinopathy

2022

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is an asymptotic and vision-threatening complication among working-age adults. To prevent blindness, a deep convolutional neural network (CNN) based diagnosis can help to classify less-discriminative and small-sized red lesions in early screening of DR patients. However, training deep models with minimal data is a challenging task. Fine-tuning through transfer learning is a useful alternative, but performance degradation, overfitting, and domain adaptation issues further demand architectural amendments to effectively train deep models. Various pre-trained CNNs are fine-tuned on an augmented set of image patches. The best-performing ResNet50 model is modified by introducing reinforced skip connections, a global max-pooling layer, and the sum-of-squared-error loss function. The performance of the modified model (DR-ResNet50) on five public datasets is found to be better than state-of-the-art methods in terms of well-known metrics. The highest scores (0.9851, 0.991, 0.991, 0.991, 0.991, 0.9939, 0.0029, 0.9879, and 0.9879) for sensitivity, specificity, AUC, accuracy, precision, F1-score, false-positive rate, Matthews’s correlation coefficient, and kappa coefficient are obtained within a 95% confidence interval for unseen test instances from e-Ophtha_MA. This high sensitivity and low false-positive rate demonstrate the worth of a proposed framework. It is suitable for early screening due to its performance, simplicity, and robustness.

show abstract

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

Cited by 3 publications

References 127 publications

A Contemporary Method on Feature Selection and Classification Using Multi-Model Deep Learning Technique for Identifying Diabetic Retinopathy

A Contemporary Method on Feature Selection and Classification Using Multi-Model Deep Learning Technique for Identifying Diabetic Retinopathy

Modified capsule network for diabetic retinopathy detection and classification using fundus images

Deep Red Lesion Classification for Early Screening of Diabetic Retinopathy

Contact Info

Product

Resources

About