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Edge/Fog Computing and Blockchain for Reliable Time-Critical Applications

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Dear Colleagues,

Edge/Fog Computing has received tremendous attention from both academia and industry since they provide reliable and diverse solutions for the time-critical application of Internet of things (IoT). Edge/fog computing not only helps reduce system latency but also improves the quality of service of time-critical applications. Edge/Fog enables artificial intelligence at the edge, which makes intelligence computing closer to users. The integration of edge/fog computing and machine learning provides smart solutions for the new development direction addressing the limitations of traditional AI-based IoT systems.

Based on the decentralization, immutability, and consensus characteristics of Blockchain, it has been applied for addressing the limitations of centralized cloud-based systems. However, Blockchain requires intensive computations and a large latency for task completion. These make Blockchain not an optimal candidate for time-critical applications. Hence, the combination of Edge/Fog Computing and Blockchain form Edge–Blockchain systems provide new possibilities for further developments that aim to improve reliability and quality of service for time-critical systems.

This “Edge/Fog Computing and Blockchain for Time-Critical Applications” Special Issue welcomes scholars, experts, and researchers in the related area to submit their contributions focusing on some of the key challenges in the context of Edge/Fog computing and Blockchain, providing new solutions and ideas. The topics of interest for this Special Issue include, but are not limited to:

New paradigms, concepts, and architectures for Edge–Blockchain systems
Edge/Fog computing-based algorithms/method design for Edge–Blockchain systems
Edge and Fog artificial intelligence for secure IoT applications
Privacy-enhancing technology in Edge–Blockchain systems
Trust-enhancing technology in Edge–Blockchain systems
Low-quality data detection techniques for Edge–Blockchain systems
Security and robustness for data-oriented Edge computing
Edge computing/machine learning for reliabilities, security, privacy, energy efficiency, trust, and data integrity in Edge–Blockchain systems
Edge-based data processing and data fusion for Edge–Blockchain systems
Big data, machine learning, AI, and analytics for Edge–Blockchain systems
Application, prototypes, and testbed for Edge–Blockchain systems

Dr. Tuan Nguyen Gia
Dr. Omar Cheikhrouhou
Guest Editors

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22 pages, 3143 KiB

Open AccessArticle

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

by Khalaf Alshamrani, Hassan A. Alshamrani, Fawaz F. Alqahtani and Bander S. Almutairi

Sensors 2023, 23(1), 235; https://doi.org/10.3390/s23010235 - 26 Dec 2022

Cited by 3 | Viewed by 2602

Abstract

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%. Full article

(This article belongs to the Special Issue Edge/Fog Computing and Blockchain for Reliable Time-Critical Applications)

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