DNA Encoding-Based Nucleotide Pattern and Deep Features for Instance and Class-Based Image Retrieval.

Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely A, C, T , and G , with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. The experimental results confirm that the proposed scheme achieves perceptible improvements compared with other state-of-the-art methods.

Diagnostic Accuracy of Artificial Intelligence-Based Algorithms in Automated Detection of Neck of Femur Fracture on a Plain Radiograph: A Systematic Review and Meta-analysis.

Objectives: To evaluate the diagnostic accuracy of artificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph. Design: Systematic review and meta-analysis. Data sources: PubMed, Web of science, Scopus, IEEE, and the Science direct databases were searched from inception to 30 July 2023. Eligibility criteria for study selection: Eligible article types were descriptive, analytical, or trial studies published in the English language providing data on the utility of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray. Main outcome measures: The prespecified primary outcome was to calculate the sensitivity, specificity, accuracy, Youden index, and positive and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CLAIM (the Checklist for AI in Medical Imaging) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies) criteria. Results: Of the 437 articles retrieved, five were eligible for inclusion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85%, with a specificity of 87%. For all studies, the pooled Youden index (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed an overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system with those of human diagnosis. The overall heterogeneity of the studies was marginal (I2 = 51%). The CLAIM criteria for risk of bias assessment had an overall >70% score. Conclusion: Artificial intelligence (AI)-based algorithms can be used as a diagnostic adjunct, benefiting clinicians by taking less time and effort in neck of the femur (NOF) fracture diagnosis. Study registration: PROSPERO CRD42022375449. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-024-01130-6.

Global Research Trends in Limb Salvage Surgery for Osteosarcoma: Findings from a Bibliometric and Visualized Analysis over 15 Years.

Background: Limb salvage surgery in osteosarcoma requires a multidisciplinary team of experts, due to which research interest has remained limited globally. This article analyzes research trends over 15 years from 2007 to 2022. Materials and Methods: Publications on limb salvage surgery in osteosarcoma were retrieved using the Web of Science. Bibliometric analysis of the publication metadata was done using R software. VOS viewer software was used to analyze the bibliographic coupling, co-citation, co-authorship, and co-occurrence to report the current trends in global research on limb salvage surgery in osteosarcoma. Results: A total of 693 articles were retrieved. On applying the inclusion and exclusion criteria, a publication metadata of 276 articles was analyzed using the methodology mentioned. Annual scientific production on the subject has shown a steady rising trend globally. China has the highest number of publications on the topic; however, the USA has the highest citations globally. The Journal "Clinical Orthopedics and Related Research" remains the pioneer in the topic with the highest number of publications and H index among all journals. Most of the research interest is generated in the developed countries of the USA, Europe, and China. Keyword analysis suggested 4 clusters of surgical reconstruction, Survival, Chemotherapy, and general management related. Newer keywords such as biological reconstructions, allograft, metastases, cell, and chemotherapy suggest future research topics in the field. Conclusion: Research interest in limb salvage surgery in osteosarcoma continues to grow with the introduction of concepts such as biological reconstructions and allografts. However, for more inclusive research on the topic, research interest must also be encouraged in underdeveloped and developing countries.

Content-Based Image Retrieval Using DNA Transcription and Translation.

DNA carries the genetic information of almost all the living beings on the earth. The flow of genetic information takes place by a series of transcription and translation reactions in which the DNA gets converted into amino-acid sequences which determine the phenotype of an organism. This property of DNA has been used in the proposed CBIR technique in which the images are first stored in DNA sequences and then their corresponding amino-acid sequences are extracted which are used to form the feature-vectors. This not only ensures the reduction of the dimension of the feature-vectors but also the preservation of the necessary information. These feature-vectors are then given as input to various classifiers for training and testing purpose. Ensemble learning is then applied to enhance the retrieval efficiency of the algorithm. The proposed algorithm is a novel approach that uses the efficiency of DNA-based computing to increase the efficiency of classifiers for image retrieval. Experimental results show that the proposed method is more efficient than the existing state-of-the-art algorithms.

An improved three-factor authentication scheme for patient monitoring using WSN in remote health-care system.

BACKGROUND AND OBJECTIVE:  Wireless sensor network-based remote health-care systems are becoming popular day by day with the rapid growth of Internet technologies and the proliferation of Internet-based application. A remote health-care system always demands a flexible and secure mechanism since any misuse of health-care related data leads to the risk of a patient's life. To make patient-related information more secure, we further consider that the patient related all the communication must be anonymous and untraceable to prevent traffic analysis. This particular approach makes the healthcare system more secure and suitable for real-time scenario. METHODS:  Recently, a three-factor mutual authentication scheme in wireless sensor networks (WSNs) is suggested by Challa et al. to deal with the security of the remote health-care system. They believe that their scheme is suitable and ensure the security of the remote health-care system. However, the authors of this article have found that their scheme suffers from sensor node capture attack; user identity reveals attack, session key leak attack, and message modification attack. Further, their scheme designs improper user revocation phase and re-registration phase, which produces the risk of illegal use of smartcard by a legitimate user. So, in this paper, the authors have given an enhanced mechanism for developing a three-factor secure mutual authentication scheme to attain effectively the security of the remote health-care system for patient monitoring. Further, the proper revocation and re-registration of users have been incorporated to support some additional securities in a case when the user lost his/her smartcard or smartcard is stolen. RESULTS AND CONCLUSIONS:  Testing with the BAN logic model affirms the accuracy of mutual authentication of the scheme designed in this paper. Also, the output of the AVISPA simulation depicts that the enhanced scheme efficiently tackle the active and passive attacks. Further, the comparative studies of our scheme with state-of-the-art schemes are also acceptable in terms of different security aspects.

An indirect watermark hiding in discrete cosine transform-singular value decomposition domain for copyright protection.

Digital image watermarking has emerged as a promising solution for copyright protection. In this paper, a discrete cosine transform (DCT) and singular value decomposition (SVD) based hybrid robust image watermarking method using Arnold scrambling is proposed and simulated to protect the copyright of natural images. In this proposed scheme, before embedding, watermark is scrambled with Arnold scrambling. Then, the greyscale cover image and encrypted watermark logo are decomposed into non-overlapping blocks and subsequently some selected image blocks are transformed into the DCT domain for inserting the watermark blocks permanently. For better imperceptibility and effectiveness, in this proposed algorithm, watermark image blocks are embedded into singular values of selected blocks by multiplying with a feasible scaling factor. Simulation result demonstrates that robustness is achieved by recovering satisfactory watermark data from the reconstructed cover image after applying common geometric transformation attacks (such as rotation, flip operation, cropping, scaling, shearing and deletion of lines or columns operation), common enhancement technique attacks (such as low-pass filtering, histogram equalization, sharpening, gamma correction, noise addition) and jpeg compression attacks.

An RGB colour image steganography scheme using overlapping block-based pixel-value differencing.

This paper presents a steganographic scheme based on the RGB colour cover image. The secret message bits are embedded into each colour pixel sequentially by the pixel-value differencing (PVD) technique. PVD basically works on two consecutive non-overlapping components; as a result, the straightforward conventional PVD technique is not applicable to embed the secret message bits into a colour pixel, since a colour pixel consists of three colour components, i.e. red, green and blue. Hence, in the proposed scheme, initially the three colour components are represented into two overlapping blocks like the combination of red and green colour components, while another one is the combination of green and blue colour components, respectively. Later, the PVD technique is employed on each block independently to embed the secret data. The two overlapping blocks are readjusted to attain the modified three colour components. The notion of overlapping blocks has improved the embedding capacity of the cover image. The scheme has been tested on a set of colour images and satisfactory results have been achieved in terms of embedding capacity and upholding the acceptable visual quality of the stego-image.