A transfer learning with deep neural network approach for diabetic retinopathy classification

Al‐Smadi, Mohammed; Hammad, Mahmoud; Baker, Qanita Bani; Al-Zboon, Sa’ad A.

doi:10.11591/ijece.v11i4.pp3492-3501

Cited by 26 publications

(20 citation statements)

References 20 publications

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“…Convolutional neural networks are used in various applications, including image classification, segmentation, and pattern recognition [19], [20]. Due to its autonomous nature has developed into a critical tool for machine vision and artificial intelligence.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A modified residual network for detection and classification of Alzheimer’s disease

Hanoon

Alasadi

2022

IJECE

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<p>Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer ResNet is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.</p>

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A modified residual network for detection and classification of Alzheimer’s disease

Hanoon

Alasadi

2022

IJECE

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“…Transfer learning [22], [23] is a machine learning method, where a model developed for a given task is reused as a reference for another model on a second task. The concept is to use the knowledge learned from the first model when solving a new problem.…”

Section: 𝑌 = 𝐷(𝐸(𝑋))mentioning

confidence: 99%

End-to-end deep auto-encoder for segmenting a moving object with limited training data

Kebir¹,

Taibi²

2022

IJECE

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<span lang="EN-US">Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.</span>

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“…By convoluting multiple filters over an image within a layer, a feature map is generated to be used, as an input, to the next layer, enabling the processing of images as pixels for such input in order to generate the required classification (in our case, diagnosis), as an output. Such a classification approach within a single classifier replaces multiple steps of the previous methods of image analysis [14], and thus, enabling a faster and more efficient image interpretation process.…”

Section: Cnn Overviewmentioning

confidence: 99%

“…CNNs and TL are the main two methods of automatic DR diagnosis using DL techniques [9], [12], [14]. TL has proven itself as a very effective technique, especially when handling domains of limited data [16].…”

Section: The Need For Transfer Learningmentioning

confidence: 99%

“…TL has proven itself as a very effective technique, especially when handling domains of limited data [16]. Instead of completely training a blank network from scratch, a feed forward approach can be used to fix weights in the lower levels that have already been optimized in order to identify the structures that can be generally detected in images, and retain the weights of the upper levels with backpropagation, enabling the model to identify the unique features of a given set of images, such as fundus images, with much less time, training examples, and computational power [14] [19]. Data are analyzed in different methods based on the complexity of the problem and the similarity to, or difference from, the data on which the neural network has been trained.…”

Section: The Need For Transfer Learningmentioning

confidence: 99%

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ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy

Al-Moosawi‬‏

Khudeyer

2021

IJCAI

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Diabetic retinopathy (DR) is an eye complication associated with diabetes, resulting in blurred vision or blindness. The early diagnosis and treatment of DR can decrease the risk of vision loss dramatically.However, such diagnosis is a tedious and complicated task due to the variability of retinal changes across the stages of the diseases, and due to the high number of undiagnosed and untreated DR cases. In this paper, we develop a computationally efficient and scalable deep learning model using convolutional neural networks (CNN), for diagnosing DR automatically. Various preprocessing algorithms are utilized to improve accuracy, and a transfer learning strategy is adopted to speed up the process. Our experiment used the fundus image set available on online Kaggle datasets. As an ultimate conclusion of applicable performance metrics, our computational simulation achieved a relatively-high F1 score of 93.2% for stage-based DR classification. Povzetek: Opisana je metoda globokih nevronskih mrež za diagnozo težav vida zaradi sladkorne bolezni.

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A transfer learning with deep neural network approach for diabetic retinopathy classification

Cited by 26 publications

References 20 publications

A modified residual network for detection and classification of Alzheimer’s disease

A modified residual network for detection and classification of Alzheimer’s disease

End-to-end deep auto-encoder for segmenting a moving object with limited training data

ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy

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