Investigation of Biomedical Cell Image Cryptography Based on RC4 Technique
Jiang, S.; Kumar, E.
Show abstract
Enormous data permeates various facets of social life, including public administration, community services, and scientific research, owing to the advent of new technologies. With the escalation of data transportation concerns, its significance in our daily lives has magnified. Particularly in the medical field, preserving information integrity during data transit has become an urgent priority. For instance, safeguarding massive electronic medical files like MRI images from cyberattacks and data loss poses a formidable challenge. Encryption and decryption techniques are employed as a solution. While numerous image types have undergone testing using cryptography techniques, biomedical images have received limited attention. In this article, We implemented RC4, pixel shuffling, and steganography to encrypt and decrypt cell images. Evaluating the encryption and decryption performance of these cell images involved testing cell viability, live/dead ratio, and morphology.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multi- Stage Feature Selection (MSFS) Algorithm for UWB- Based Early Breast Cancer Size Prediction 97%
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 96%
- Prediction and control of COVID-19 infection based on a hybrid intelligent model 95%
Similar papers in this journal
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 95%
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 94%
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 94%
Similar papers in this journal
- Accurate detection of non-proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural networks 93%
- HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification 93%
- Modeling and Visualization of Rice Root Based on Morphological Parameters 93%
Similar papers in this journal
- Hilbert-Envelope Features for Cardiac Disease Classification from Noisy Phonocardiograms 92%
- Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification 91%
- A Transfer Entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective 90%
Similar papers in this journal
- Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights 92%
- Unsupervised Discovery of Risk Profiles on Negative and Positive COVID-19 Hospitalized Patients 92%
- A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.