Security and Privacy of Cloud- and IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network

Area of Science:

• Cloud Computing Security
• Medical Data Storage
• Image Processing in Healthcare

Background:

• Healthcare generates vast data daily from medical devices, necessitating secure storage and processing.
• Cloud computing offers solutions for handling large medical datasets securely against various attacks.
• Existing methods for medical image analysis in cloud environments require enhanced security and accuracy.

Purpose of the Study:

• To propose a secure cloud computing framework for medical data storage and disease prediction.
• To develop an extended zigzag image encryption scheme for enhanced data security against attacks.
• To introduce a fuzzy convolutional neural network (FCNN) for accurate medical image classification.

Main Methods:

• Utilized an extended zigzag image encryption scheme for securing medical data in the cloud.
• Implemented a fuzzy convolutional neural network (FCNN) for image classification tasks.
• Trained the FCNN on decrypted medical images for cancer level classification.
• Experimentation conducted using a standard medical image dataset.

Main Results:

• The proposed extended zigzag encryption scheme demonstrated high tolerance to data attacks.
• The FCNN algorithm achieved effective classification of medical images.
• The system successfully classified cancer levels with different training layers.
• Experimental results indicated superior performance compared to existing algorithms.

Conclusions:

• The developed secure cloud computing approach is effective for medical image diagnosis.
• The combination of extended zigzag encryption and FCNN offers a robust solution for secure and accurate medical image analysis.
• This method can significantly aid doctors and patients in timely treatment decisions.