A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks

Conti, Vincenzo; Militello, Carmelo; Rundo, Leonardo; Vitabile, Salvatore

doi:10.1109/tetc.2020.3018312

Cited by 41 publications

(14 citation statements)

References 58 publications

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“…The computational time for the proposed process is computed and is observed to possess a reduced duration of 11.21 msec. This observed value is proved to be lesser than the other computer vision processes [29].…”

Section: Resultsmentioning

confidence: 60%

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Palaniswamy

Vellingiri

2023

IEEE Access

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Recently, the Internet of Things (IoT) and computer vision technologies find useful in different applications, especially in healthcare. IoT driven healthcare solutions provide intelligent solutions for enabling substantial reduction of expenses and improvisation of healthcare service quality. At the same time, Diabetic Retinopathy (DR) can be described as permanent blindness and eyesight damage because of the diabetic condition in humans. Accurate and early detection of DR could decrease the loss of damage. Computer-Aided Diagnoses (CAD) model based on retinal fundus image is a powerful tool to help experts diagnose DR. Some traditional Machine Learning (ML) based DR diagnoses model has currently existed in this study. The recent developments of Deep Learning (DL) and its considerable achievement over conventional ML algorithms for different applications make it easier to design effectual DR diagnosis model. With this motivation, this paper presents a novel IoT and DL enabled diabetic retinopathy diagnosis model (IoTDL-DRD) using retinal fundus images. The presented Internet of Things Deep Learning -Diabetic Retinopathy Diagnosis (IoTDL-DRD) technique utilizes IoT devices for data collection purposes and then transfers them to the cloud server to process them. Followed by, the retinal fundus images are preprocessed to remove noise and improve contrast level. Next, mayfly optimization based region growing (MFORG) based segmentation technique is utilized to detect lesion regions in the fundus image. Moreover, densely connected network (DenseNet) based feature extractor and Long Short Term Memory (LSTM) based classifier is used for effective DR diagnosis. Furthermore, the parameter optimization of the LSTM method can be carried out by Honey Bee Optimization (HBO) algorithm. For evaluating the improved DR diagnostic outcomes of the IoTDL-DRD technique, a comprehensive set of simulations were carried out. A wide ranging comparison study reported the superior performance of the proposed method. INDEX TERMS Computer aided diagnosis, Deep learning, Diabetic retinopathy, Fundus images, Honey bee optimization.

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Section: Resultsmentioning

confidence: 60%

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Palaniswamy

Vellingiri

2023

IEEE Access

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“…Moreover, we set default parameters for loss weightings (e.g., the focusing parameter for focal weighting) based on the previous studies, but tuning such parameters would enable the performance improvement of FCNs. Furthermore, in this study, we focused on segmenting brain structures, including blood vessels, from the MR images of patients with cerebral aneurysms, but considering the clinical practice, it would be desired to automatically detect the location of aneurysms, as in [37], in addition to the segmentation.…”

Section: Limitationsmentioning

confidence: 99%

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

et al. 2021

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Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address this problem, we investigated how loss weighting strategies work for brain structure segmentation tasks with different class imbalance situations on MR images. In this study, we adopted segmentation tasks of the cerebrum, cerebellum, brainstem, and blood vessels from MR cisternography and angiography images as the target segmentation tasks. We used a U-net architecture with cross-entropy and Dice loss functions as a baseline and evaluated the effect of the following loss weighting strategies: inverse frequency weighting, median inverse frequency weighting, focal weighting, distance map-based weighting, and distance penalty term-based weighting. In the experiments, the Dice loss function with focal weighting showed the best performance and had a high average Dice score of 92.8% in the binary-class segmentation tasks, while the cross-entropy loss functions with distance map-based weighting achieved the Dice score of up to 93.1% in the multi-class segmentation tasks. The results suggested that the distance map-based and the focal weightings could boost the performance of cross-entropy and Dice loss functions in class imbalanced segmentation tasks, respectively.

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“…In medical imaging, the U-Net model proposed by Ronneberger et al [22] has established an inflection point in the medical community, demonstrating that a deep convolutional neural network can be trained with small datasets, which is often necessary given the time-demanding task of obtaining labeled images from specialists, and still achieve competitive results. Several U-net-like architectures have been proposed to attend to this issue for different tasks in various applications, including segmentation of cerebral blood vessels, brain tumors, ischemic-stroke lesions, aneurysms, and skin lesions [23][24][25][26][27][28][29]. Concatenations of U-Nets have also been demonstrated to help improve the segmenting of smaller blood vessels in retina fundus images [30].…”

Section: Introductionmentioning

confidence: 99%

Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes

et al. 2022

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Segmenting vessels in brain images is a critical step for many medical interventions and diagnoses of illnesses. Recent advances in artificial intelligence provide better models, achieving a human-like level of expertise in many tasks. In this paper, we present a new approach to segment Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) images, relying on fewer training samples than state-of-the-art methods. We propose a conditional generative adversarial network with an adapted generator based on a concatenated U-Net with a residual U-Net architecture (UUr-cGAN) to carry out blood vessel segmentation in TOF-MRA images, relying on data augmentation to diminish the drawback of having few volumes at disposal for training the model, while preventing overfitting by using regularization techniques. The proposed model achieves 89.52% precision and 87.23% in Dice score on average from the cross-validated experiment for brain blood vessel segmentation tasks, which is similar to other state-of-the-art methods while using considerably fewer training samples. UUr-cGAN extracts important features from small datasets while preventing overfitting compared to other CNN-based methods and still achieve a relatively good performance in image segmentation tasks such as brain blood vessels from TOF-MRA.

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A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks

Cited by 41 publications

References 58 publications

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks

Dual U-Net-Based Conditional Generative Adversarial Network for Blood Vessel Segmentation with Reduced Cerebral MR Training Volumes

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