Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique
Chandra Shekhar Tiwari, Vijay Kumar Jha · International Journal of Image Graphics and Signal Processing · 2022
To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes.This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud.K-Means is used for segmenting images into pixels and auto-encoders to remove noise (denoising); the Random Forest regressor, tree-method based ensemble model is used for classification.The study obtained CT scan-images as datasets from 'Kaggle' and classifies the images into 'Non-Covid' and 'Covid' categories.The software utilized is Jupyter-Notebook, in Python.PSNR with MSE evaluation metrics is done using Python.Through testing-and-training datasets, lower MSE score ('0') and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images.