Secure Framework For Image Classification Using CNN Based Model

Abstract

Image processing, empowered by machine learning (ML), has transformed applications in medical imaging, autonomous vehicles, facial recognition, and industrial quality control. This article explores advanced ML techniques, including convolutional neural networks (CNNs), transfer learning, and generative adversarial networks (GANs), to enhance image analysis accuracy and efficiency. The study objectives include developing a CNN-based model for medical image classification, evaluating its performance against traditional methods, and integrating blockchain for secure data handling. Using a dataset of 10,000 medical images, the methodology employs preprocessing, CNN training with five convolutional layers, and performance evaluation via accuracy, precision, and recall metrics. Results demonstrate a 92% accuracy, surpassing traditional approaches like support vector machines (85%), with blockchain ensuring data integrity. Implementation insights highlight cloud deployment and scalability, though challenges like data bias and computational costs persist. The article discusses limitations, such as false positives and training demands, proposing deep learning advancements and edge computing as future directions. This study underscores ML’s transformative potential in image processing, offering a scalable, secure framework for real-world applications and paving the way for innovations in generative models and decentralized data systems.