A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Zia, Farrukh; Irum, Isma; Qadri, Nadia N.; Nam, Yunyoung; Khurshid, Kiran; Ali, Muhammad Junaid; Ashraf, Imran; Khan, Muhammad Attique

doi:10.32604/cmc.2022.017820

Cited by 16 publications

(8 citation statements)

References 44 publications

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“…Therefore, this work can play a vital role in the automated recognition and classification of the glaucomatous-affected regions. In the future, we plan to implement some feature section techniques and employed on deep learning models [3,[46][47][48][49][50]. Also our plan is to evaluate the work on other eye diseases.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Nawaz

Nazir

Javed

et al. 2022

Sensors

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Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.

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Section: Discussionmentioning

confidence: 99%

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Nawaz

Nazir

Javed

et al. 2022

Sensors

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“…By picking the most significant features and discarding superfluous attributes, feature selection enhances the ML, DL approaches experience and raises the prognostic capacity of these algorithms [ 148 ]. On the other side, feature fusion is the process of combining training picture feature vectors generated from the common weighted network layer with feature vectors consisting of other statistical information such that the suggested framework can use quite so many features for categorization [ 210 ]. Thus, feature selection and fusion of the selected features plays a major role to bring off better output in terms of accuracy and other performance measures.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

“…Prediction accuracy is not up to the mark. Farrukh Zia et al, 2021 [ 210 ] Feature Selection and Feature Fusion VGG and Inception V3 Kaggle Accuracy – 96.4% Comparatively takes less time. Different classification methods are applied for dataset testing.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

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“…A machine learning approach was suggested to determine the main causes of DR in individuals with elevated glucose levels [32]. Employing transfer learning approaches, this process isolates and organizes the features of DR into many classes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Applied Sciences

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity.

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A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Cited by 16 publications

References 44 publications

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

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