Automatic Segmentation and Detection System for Varicocele in Supine Position

AlZoubi, Omar; Awad, Mohammad Abu; Abdalla, Ayman M.

doi:10.1109/access.2021.3111021

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…One way we might try to lessen the quantity of training data needed and avoid overfitting our model is by using image augmentations. Augmenting images is a way to generate more training data on purpose [33].…”

Section: Employed Methods In the Proposed Skin Disease Classification...mentioning

confidence: 99%

Big Dermatological Data Service for Precise and Immediate Diagnosis by Utilizing Pre-trained Learning Models

Elbes,

AlZu'bi,

Kanan

et al. 2023

Preprint

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Artificial intelligence (AI) approaches have been shown to be effective in classifying skin diseases and outperforming dermatologists in diagnosis. Using big data as a dermatological diagnosis service can present several challenges. One challenge is the need to accurately label and classify large amounts of data, such as images of infected skin. This can be time-consuming and resource-intensive. It is important to implement proper safeguards to protect sensitive medical information. Despite these challenges, the use of big data in dermatology can lead to several outcomes. It can improve the accuracy of diagnoses, leading to better patient outcomes. It can also help to identify patterns and trends in skin conditions, allowing for earlier detection and prevention. In addition, big data can be used to identify risk factors for certain conditions, enabling targeted preventative measures. Convolutional neural networks (CNNs) have been widely used for skin lesion classification, and recent advances in machine learning algorithms have led to a decrease in misclassification rates compared to manual categorization by dermatologists. This article utilizes the use of big data for accurate dermatological diagnosis services, it introduces the use of various CNNs for classifying different types of skin cancer. While deep learning and pretrained transfer learning techniques have advantages over traditional methods, they also have limitations and the potential for incorrect identification under certain circumstances. This work discusses these vulnerabilities and employs pretrained models to classify 11 different skin diseases, including monkey pox. The performance of the system is evaluated using accuracy, loss, precision, recall, and F1 score, and the results show that the system is able to diagnose the 11 skin illnesses with an accuracy rate of 97% using transfer learning in Keras and computer vision models. The best model was found to be Inception_ResNetV2 with 50 epochs and the Adam optimizer.

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Section: Employed Methods In the Proposed Skin Disease Classification...mentioning

confidence: 99%

Big Dermatological Data Service for Precise and Immediate Diagnosis by Utilizing Pre-trained Learning Models

Elbes,

AlZu'bi,

Kanan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In the enhancement stage, the image quality is processed to reduce the noise effect, especially in images that have been captured in an unconstrained environment. The segmentation step defines the Region of Interest (ROI) in the image, such as with extracting the iris ring from the whole eye image [3] or the veins region in a medical image [4,5]. Furthermore, some systems such as face recognition may require multiple regions of interest [6].…”

Section: Introductionmentioning

confidence: 99%

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Rifaee¹,

Abdallah²,

Salah³

et al. 2022

Computers, Materials &Amp; Continua

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Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.

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