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Manually segmenting breast lesion images poses a labor-intensive and expensive undertaking for radiologists. Therefore, the adoption of an automated diagnostic approach becomes imperative, aiming to precisely segment breast lesions and mitigate the associated challenges. The segmentation of malignant regions is a crucial process in the comprehensive examination of breast Magnetic Resonance Imaging (MRI). However, achieving proficient segmentation through the effective utilization of deep learning methodologies continues to pose a formidable challenge. The complexity of the task is underscored by the substantial variations evident in the dimensions, structural attributes, and visual characteristics inherent to diverse malignancy types. This research proposes an Enhance Edge U-Net (EEU-Net) model to address these concerns. In our proposed model, a Deep Convolutional Neural Network (DCNN) is employed that follows an architecture comprising both an encoder and a decoder, drawing inspiration from the U-Net framework. The utilization of the EEU-Net model facilitates enhanced tumor localization through the integration of MRI data associated with boundaries with the primary data obtained from breast MRI. In the decoding stage, the information pertaining to boundaries is derived from source MRI scans with diverse scales combined with the relevant nearby surrounding information. To enhance the performance of this segmentation model, a loss function is incorporated. The incorporation of boundary information in this loss function enhances the learning process, resulting in more precise outcomes. The results of our experiments indicate that EEU-Net is superior in efficiency and reliability pertaining to the segmentation of breast tumors as compared to other techniques. It outperforms alternative segmentation methods in terms of the Jaccard and Dice coefficients when evaluated on the publicly accessible RIDER breast cancer MRI dataset. Specifically, EEU-Net achieved Jaccard and Dice coefficients of 0.804(±0.024) and 0.884(±0.017), respectively, on the RIDER breast MRI datasets. Additionally, the statistical analysis for the proposed model, utilizing a paired t-test, uncovered a noteworthy difference with a p -value less than 0.05. [ABSTRACT FROM AUTHOR]

In recent years, facial recognition technology has become increasingly integrated into society, making privacy protection crucial. Previous techniques offered minimal secrecy safeguards through simple obscuration methods. This paper addresses the strict privacy requirements of face image data by developing a novel framework that synergistically integrates Generative Adversarial Networks (GANs), clustering algorithms, and Blockchain technology. The methodology proposes a cutting-edge Privacy-Preserving Self-Attention GAN (PPSA-GAN) to generate realistic synthetic facial imagery. An integrated mini-batch K-means clustering algorithm anonymizes these images into distinct groupings, maximizing privacy preservation. Blockchain integration complements the system by fortifying trust through decentralized ledgers for transparent yet secure data storage and auditing. Rigorous benchmarking on the CelebA dataset confirms the PPSA-GAN architecture's state-of-the-art performance, attaining an impressive Inception Score of 13.99 and a Fréchet Inception Distance of 35.50. The mini-batch clustering forms 125 distinct clusters, effectively anonymizing facial attributes within the synthetic images. Blockchain integration further bolsters privacy assurances via tamper-proof historical records, showcasing precision, recall, F1-score, and accuracy values of 0.948, 0.938, 0.943, and 0.947, respectively. This multifunctional framework represents a novel contribution, fostering an ethical technological ecosystem that balances progress and privacy. Prospective deployment horizons encompass identity verification, surveillance infrastructure, and augmentation of medical image repositories, seeding an enlightening future for facial recognition domains. [ABSTRACT FROM AUTHOR]