Secure medical image transmission using deep neural network in e‐health applications

Alarood, Ala Abdulsalam; Faheem, Muhammad; Al‐Khasawneh, Mahmoud Ahmad; Alzahrani, Abdullah I. A.; Alshdadi, Abdulrahman A.

doi:10.1049/htl2.12049

Cited by 26 publications

(5 citation statements)

References 48 publications

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“…The majority vote determines which category will be chosen. Deep feature level fusion has great potential to improve classification performance 57 , 58 since it combines many feature sets produced from different feature extractors. When many perspectives (in‐depth features obtained from multiple CNNs) must be represented, feature‐level fusion often involves concatenating numerous normalized feature subsets into a single feature vector.…”

Section: Methodsmentioning

confidence: 99%

Lyme rashes disease classification using deep feature fusion technique

Ali,

Anwar,

Nauman

et al. 2023

Skin Research and Technology

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Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists’ probe and investigate Lyme skin rashes effectively. This paper proposes a new in‐depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state‐of‐the‐art models.

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Section: Methodsmentioning

confidence: 99%

Lyme rashes disease classification using deep feature fusion technique

Ali,

Anwar,

Nauman

et al. 2023

Skin Research and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the advancement of machine learning (ML) and deep learning (DL) techniques, these have been widely applied in various fields, such as lyme rashes disease recognition [16,17], facial expression identification [18,19], sentimental analysis [20], health monitoring [21,22], gender classification [23] and medical disease diagnosis [24][25][26]. Numerous studies [27,28] analysed the significance of different characteristics that might be used to diagnose ASD, such as behavioural patterns, facial appearance, eye tracking, and speech.…”

Section: Introductionmentioning

confidence: 99%

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks

Ahmad,

Rashid,

Faheem

et al. 2024

Healthcare Tech Letters

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Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state‐of‐the‐art models in terms of accuracy and computational cost.

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“…The IoMT has been a gamechanger in the medical field by supplying real-time patient data and empowering doctors to provide better care and see better patient results. [5][6][7] This technology is essential in today's healthcare systems because of its ability to expand patient access to care, lower hospitalization rates, and increase the prevalence of preventative medicine through constant monitoring.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Akram,

Rashid,

Jaffar

et al. 2023

Skin Research and Technology

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IntroductionParticularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.MethodThis research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset.ResultsThe experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well.ConclusionIn conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

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Secure medical image transmission using deep neural network in e‐health applications

Cited by 26 publications

References 48 publications

Lyme rashes disease classification using deep feature fusion technique

Lyme rashes disease classification using deep feature fusion technique

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

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