Application of Image Processing Techniques for Early Detection of Diseases in Healthcare Technologies Using Deep Learning
Deep learning applications have significantly impacted medical image analysis. Ophthalmologists may now quickly identify a range of eye disorders and the degree of severity of associated patient issues thanks to these applications. These algorithms can help in the identification and diagnosis of a number of diseases, including tumours, lesions, anatomical anomalies, and pathological alterations, in medical picture analysis. They can also help in assessing prognosis, responsiveness to treatment, and the course of the disease. A deep learning approach can better evaluate the picture data and imitate the way the human brain processes images for challenges related to image identification, classification, and other areas. It has been widely employed in various fields and performs excellently in large scale image processing. Clinical notes, lab test results, diagnoses, prescriptions, and other structured and unstructured data may all be analysed by deep learning models at very high rates and with the highest levels of accuracy from electronic health records.
Deep learning has a wide range of possible uses in artificial intelligence in the future. These cover a wide range of topics, such as computer vision, robotics, recommendation systems, natural language processing, autonomous cars, fraud detection, and more. In machine vision, image recognition refers to a program's capacity to recognize elements such as people, places, objects, writing, and motion in digital images. Computers can recognize images by combining a camera, artificial intelligence software, and machine vision technology. Neural networks are used in deep learning to extract meaningful feature representations from input directly. A pre trained neural network, for instance, can be used to recognize and eliminate picture artefacts like noise. Another popular use of machine learning in the healthcare industry is predictive analytics of medical data. Massive datasets can be analysed by AI and ML algorithms to identify patterns and trends that can subsequently be used to forecast future events. The use of machine learning in healthcare include risk assessment instruments, patient monitoring apps, and diagnostic support systems. These technologies can give clinicians insights from large datasets that can help them make better judgments. Ultrasound, nuclear medicine, magnetic resonance imaging, and radiography are the primary imaging modalities employed in modern medicine. help go into more depth about these later. Electromagnetic radiation is used in radiography to create internal body images. X rays are the most popular and widely used type of radiography.
Digital health information gathered from devices that are worn is one type of enormous quantity of data that may be processed by medical image analysis algorithms. In addition to promoting health and wellbeing, the algorithms can be used to control diseases and health concerns. Data mining techniques can also be used for MRI image processing. Pre processing is the initial step in these procedures. objects are separated in images using segmentation. Finally, features such as colour, form, and texture are extracted. Finally, the brain tumour is identified through classification. An artificial intelligence technique termed deep learning trains machines to digest information in a manner similar to that of the human brain. To generate precise insights and forecasts, deep learning models are able to identify intricate patterns in images, text, sounds, and other types of data. Contributions are invited from a range of disciplines and perspectives, including, but not restricted to: Application of image processing techniques for early detection of diseases in healthcare technologies using deep learning.
Topics of interest for this special issue include:
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A deep learning model that uses deep convolutional neural networks to detect and analyse disease.
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Methods for signal and image processing in the creation of intelligent medical systems.
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Deep neural networks for safe medical picture transfer in electronic health records.
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Comprehensive analysis of artificial intelligence and deep learning based smart health monitoring.
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A comprehensive investigation of deep learning in relation to medical image processing.
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Deep learning model for medical picture classification with optimal feature selection in the Internet of Medical Things.
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Applying deep learning based on images to identify medical diagnosis and curable diseases.
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A Deep Convolutional Neural Network for the Early Detection of Heart Disease.
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Application of deep machine learning to the early identification of ageing related neurodegenerative disorders.
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IoT health system with deep learning capabilities to diagnose blindness through retinal picture analysis.
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Malignant skin cancer early detection with image processing and deep learning.
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Using deep learning approaches, leaf disease diagnosis may be done automatically and reliably.
Proposed Tentative Timeline for Submission:
First Submission Deadline: 25 August, 2024
Notification of First Round Decision: 25 October, 2024
Revised Paper Submission Deadline: 30 November, 2024
Notification of Final Decision: 30 January, 2025
Final Paper Submission Deadline: 31 March, 2025
Proposed Guest Editors:
Dr. Fahad Taha AL-Dhief
School of Electrical Engineering,
Faculty of Engineering,
Universiti Teknologi Malaysia, (UTM),
Johor Bahru, Johor, Malaysia
E-mail: taha-1989@graduate.utm.my, tahaaldhief@hotmail.com
Google Scholar: https://scholar.google.com/citations?user=Dnge80MAAAAJ&hl=en
Biography:
Fahad Taha Al-Dhief received the B.S. degree in software engineering from Imam Ja’afar Al-Sadiq University, Iraq, in 2013, and the M.S. degree in computer science from the University Kebangsaan Malaysia, Malaysia, in 2016. He completed his Ph.D. degree with the Department of Communication Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia. His research interests include sensor networks, routing protocols, mobile ad-hoc networks, social networks, the Internet of Things, machine learning, artificial neural networks, deep learning, and location-based service.
Dr. Musatafa Abbas Abbood Albadr
Department of Oil and Gas Engineering,
Basrah University for Oil and Gas,
Basra 1004, Iraq
E-mail: mustafa.albadr@buog.edu.iq
Google Scholar: https://scholar.google.com/citations?user=UceWEXkAAAAJ&hl=en
Biography:
Musatafa Albadr has received his Ph.D. degree in Information Science and Technology from
University Kebangsaan Malaysia, Malaysia, in 2021. Currently, he is a lecturer at the Department of Oil and Gas Engineering, Basrah University for Oil and Gas, Al-Basrah 61004, Iraq. His research interests are Machine learning, Artificial Neural Networks, Deep Learning, Optimization, Speech Processing, Healthcare Technologies, Image Processing, and Steganography Techniques.
Dr. Selim Hossain
Department of Electronics and Communication Engineering (ECE)
Faculty of Computer Science and Engineering
Hajee Mohammad Danesh Science & Technology University,
Dinajpur, Bangladesh.
E-mail: selim.ece@tch.hstu.ac.bd
Google Scholar: https://scholar.google.com/citations?user=6rqh2NwAAAAJ&hl=en
Biography:
Selim Hossain is a Lecturer in Electronics and Communication Engineering at Hajee Mohammad Danesh Science & Technology University. With a background in Telecommunication Engineering and an M.Sc. in ICT, he specializes in IoT, Blockchain, and Machine Learning. With prior roles in network engineering and lecturing, he brings practical insights to academia. Dedicated to research and innovation, Selim aims to bridge theoretical knowledge with real-world applications in emerging technologies