Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique

Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique

Автор: Chandra Shekhar Tiwari, Vijay Kumar Jha

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 4 vol.14, 2022 года.

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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 (de-noising); 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.

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Arnoldcat mapping, Playfair ciphering, k-means, random forest, image encryption, image decryption, medical image security, security enhancements

Короткий адрес: https://sciup.org/15018496

IDR: 15018496   |   DOI: 10.5815/ijigsp.2022.04.02

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