DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network

Alzubaidi, Laith; Fadhel, Mohammed A.; Oleiwi, Sameer Razzaq; Al-Shamma, Omran; Zhang, Jinglan

doi:10.1007/s11042-019-07820-w

Cited by 115 publications

(103 citation statements)

References 41 publications

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“…This CNN has been employed to solve several computer vision tasks [19,20]. It has shown great performance in different medical applications [21,22].…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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“…This CNN has been employed to solve several computer vision tasks [19,20]. It has shown great performance in different medical applications [21,22].…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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“…Deep learning has achieved impressive performance in several tasks including visual recognition, language, and speech detection systems, besides drawing attention to its research sites and considerable advances [ 36 , 37 , 38 ]. Conversely, due to the lack of general public data availability and their challenging nature, numerous fields are almost have not considered yet by DNNs.…”

Section: Deep Learning Challengesmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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“…The handcrafted features are utilized for DFU classification which is selected manually, whereas in deep learning methodologies features extraction process is performed automatically [20]. Pre-trained CNN models are used for DFU detection [6,21,22]. The DFUnet is employed to classify the foot lesions into healthy/unhealthy classes [23].…”

Section: Related Workmentioning

confidence: 99%

“…Later, two fully connected (FC) layers are used, where input data is multiplied to the weight matrix and added with the bias as depicted in Figure 6. The features map of the proposed CNN model with existing pre-trained AlexNet model and DFU_QUTNet [43] is illustrated in the Figure 7-8. In Figure 7-8, features map comparison shows that learning patterns contains highest information compared to existing CNN models.…”

Section: Iiia Classification Of Diabetic Foot Ulcermentioning

confidence: 99%

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

et al. 2020

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DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network

Cited by 115 publications

References 41 publications

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

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