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Abhay Kumar Yadav, Virendra P. Vishwakarma
No abstract is available for this record.
Sonali Rokade, Nilamadhab Mishra
No abstract is available for this record.
Muhammad Ahmad Nawaz Ul Ghani, Kun She, Muhammad Arslan Rauf, Masoud Alajmi · 6 authors
In recent years, facial recognition technology has become increasingly integrated into society, making privacy protection crucial. Previous techniques offered minimal secrecy safeguards through simple obscuration methods. This paper addresses the strict privacy requirements of face image data by developing a novel framework that synergistically integrates Generative Adversarial Networks (GANs), clustering algorithms, and Blockchain technology. The methodology proposes a cutting-edge Privacy-Preserving Self-Attention GAN (PPSA-GAN) to generate realistic synthetic facial imagery. An integrated mini-batch K-means clustering algorithm anonymizes these images into distinct groupings, maximizing privacy preservation. Blockchain integration complements the system by fortifying trust through decentralized ledgers for transparent yet secure data storage and auditing. Rigorous benchmarking on the CelebA dataset confirms the PPSA-GAN architectureâs state-of-the-art performance, attaining an impressive Inception Score of 13.99 and a FrĂ©chet Inception Distance of 35.50. The mini-batch clustering forms 125 distinct clusters, effectively anonymizing facial attributes within the synthetic images. Blockchain integration further bolsters privacy assurances via tamper-proof historical records, showcasing precision, recall, F1-score, and accuracy values of 0.948, 0.938, 0.943, and 0.947, respectively. This multifunctional framework represents a novel contribution, fostering an ethical technological ecosystem that balances progress and privacy. Prospective deployment horizons encompass identity verification, surveillance infrastructure, and augmentation of medical image repositories, seeding an enlightening future for facial recognition domains.
Michael J. Diaz, Jasmine Tran
Citation: Diaz MJ, Tran JT. Potential Benefits of Non-Fungible Tokens (NFTs) and Blockchain Technology in Dermatology. Dermatol Pract Concept. 2024;14(1):e2024061. DOI: https://doi.org/10.5826/dpc.1401a61
K Rajeshkumar, Chidambaram Ananth, N. Mohananthini
Blockchain (BC) and Machine learning (ML) technologies have been investigated for potential applications in medicine with reasonable success to date. On the other hand, as accurate and early diagnosis of skin lesion classification is essential to gradually increase the survival rate of the patient, Deep-Learning (DL) and ML technologies were introduced for supporting dermatologists to overcome these challenges. This study designed a Blockchain Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using an Optimal Deep Learning (BHESKD-ODL) model. The presented BHESKD-ODL model achieves security and proper classification of skin lesion images using BC to store the medical images of the patients to restrict access to third-party users or intruders. In addition, the BHESKD-ODL method secures the medical images using the mayfly optimization (MFO) algorithm with the Homomorphic Encryption (HE) technique. For skin lesion diagnosis, the proposed BHESKD-ODL method uses pre-processing and the Adam optimizer with a Fully Convolutional Network (FCN) based segmentation process. Furthermore, a radiomics feature extraction with a Bidirectional Recurrent Neural Network (BiRNN) model was employed for skin lesion classification. Finally, the Red Deer Optimization (RDO) algorithm was used for the optimal hyperparameter selection of the BiRNN approach. The experimental results of the BHESKD-ODL system on a benchmark skin dataset proved its promising performance in terms of different measures.
MarĂa J. Peregrina-PĂ©rez, JesĂșs Lagares-GalĂĄn, Juan Boubeta-ÂPuig
No abstract is available for this record.
Jaclyn B. Anderson, Melissa Laughter
No abstract is available for this record.
M. Ramanan, Laxman Singh, A. Suresh Kumar, Aakaash Suman Suresh · 7 authors
No abstract is available for this record.
Vijayasri Iyer, A Vyshnavi, Sriram Iyer, P. K. Krishnan Namboori
In the pharmacogenomic and theranostic approach of treating melanoma, a continuous monitoring of the disease and the mutations associated with the disease is essential. Such a monitoring system has been designed and developed based upon the concept `One-shot learning', a machine learning technique adapted to work with a relatively small number of training images. The samples have been exhaustively studied through genomics, epigenomics, metagenomics and environmental genomics, finding the genetic signature behind proneness of these attributes. The mutations CDK4, CDKN2A, BRAF and KIT have been included in the analysis. The prediction accuracy of the machine is found to substantially high suggesting the device for the theranostic and pharmacogenomic strategies of controlling melanoma. A Distributed Ledger Technology (DLT) based system has been proposed for real time data sharing, training and analysis enabling hospitals and research labs to communicate with each other and conduct a cost-effective diagnostic workflow.
Joe K. Tung, Vinod E. Nambudiri
Dear Editor, Since its initial popularization in 2008 as the underpinnings of the digital currency Bitcoin, blockchain has seen its implications spread beyond the financial industry.1 The field of dermatology presents promising potential applications for this burgeoning technology. Blockchain facilitates communication on a peerâtoâpeer platform with users sharing data directly with each other (Fig. 1). Computational algorithms ensure that the database is permanent, chronologically ordered and universally available on a network while remaining cryptographically secure. These attributes allow blockchain to remove intermediary costs, reduce manual errors and decrease risks of single points of failure.1 Potential implementation of blockchain technology in dermatology. (1) Dermatologists store encrypted patient data via blockchain. The stored data are securely distributed across the entire network of participating parties. (2) Data can be decrypted by participating parties and patients, using a private digital key. (3) Once decrypted, data can be used for various applications. Secure data storage and distribution are particularly useful for dermatology. Given our field's visual nature, digital imaging has become ubiquitous for documenting diseases, following patient progression and assessing treatment efficacy. With increasing image acquisition, needs for better standardization of imaging techniques and storage system interoperability have emerged.2 Blockchain offers the ideal solution. Encrypted images can be stored via blockchain, with image ownership and locations encoded as transactions. Every participating party could access data from other clinical practices, as long as patient permission and a secure digital decryption key were obtained. As information would be duplicated throughout the network â not backed up at individual institutions â this approach could reduce singleâsite storage capacity needs. Patients could also access medical records via private digital keys, selectively sharing information while retaining data control. This model would eliminate burdens of printing patient records, physically transferring images or repeating unnecessary biopsies when patients change dermatologists. Patients could more easily obtain second opinions, grant viewership rights to guardians, or provide information for clinical research. Dermatologists would have enhanced focus on collaboration, care coordination and outcomesâbased care. Blockchain technology may also expedite machine learning advances in dermatology. Recent innovations highlight potential roles for artificial intelligence in skin cancer diagnosis.3 Allowing machine learning algorithms access to numerous images on a secure blockchain network would drive further optimization of computerâassisted analysis. Importantly, blockchain would provide immutable trails between what the algorithms suggested and actual diagnoses, allowing for continuously learning feedback loops and improved diagnosis. A best practice for implementation of blockchain includes data encryption to ensure confidentiality. Speed and scalability are being addressed with ongoing research, and the costâeffectiveness of implementation will also need to be explored.4 As these challenges are addressed, dermatology has the opportunity to lead other specialties in harnessing this emerging technology's potential to revolutionize the standardization, storage and distribution of information. Funding sources: None. Conflicts of interest: None to declare.
ShihâHsiung Lee, Chu Sing Yang
In traditional Chinese medicine, the growth situation of the surface of nails reflects the physiological condition of the human body. Diagnosis by nail can effectively predict and prevent disease. Human nails have a high degree of uniqueness, and it can be used for biometric recognition. In this work, microscope sensor was used to capture the clear image and segment the lunula and nail plate effectively through image preprocessing. Fingernailsâ image is managed as the identity authentication. Histogram of oriented gradients and local binary patterns are used to capture the characteristic value. It uses support vector machine and random forest tree for classification. The performance of each feature extraction algorithm was analyzed for the two classifiers and the deep neural network algorithm was used comparatively. Furthermore, the security and privacy of the Internet of Things is still a challenge. This work uses the highly anonymous blockchain technology to effectively protect data privacy and manage each userâs data through the blockchain, in which any change or manipulation can be recorded and tracked, and the data security is improved. Therefore, this article presents a nail analysis management system with the use of microscopy sensor and blockchain.
Katiuscia Mannaro, Gavina Baralla, Andrea Pinna, Simona Ibba
The use of teledermatology in primary care has been shown to be reliable, offering the possibility of improving access to dermatological care by using telecommunication technologies to connect several medical centers and enable the exchange of information about skin conditions over long distances. This paper describes the main points of a teledermatology project that we have implemented to promote and facilitate the diagnosis of skin diseases and improve the quality of care for rural and remote areas. Moreover, we present a blockchain-based approach which aims to add new functionalities to an innovative teledermatology platform which we developed and tested in the Sardinian Region (Italy). These functionalities include giving the patient complete access to his/her medical records while maintaining security. Finally, the advantages that this new decentralized system can provide for patients and specialists are presented.