Deep Learning in Medical Image Analysis: Foundations, Techniques, and Applications
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 14295
Special Issue Editor
Special Issue Information
Dear Colleagues,
Deep learning is a state-of-the-art machine learning approach. The success of deep learning in many pattern recognition applications has caused excitement and high expectations, and deep learning can bring revolutionary changes in health care and disease diagnosis. The goal of this Special Issue is to bring together recent advances and cutting-edge research in the use of deep learning in medical image analysis. It also aims to provide a comprehensive overview of the current state of the art and to highlight the challenges, opportunities, and future directions of this rapidly evolving field. The purpose of this Special Issue, “Deep Learning in Medical Image Analysis”, is to present and highlight novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.
Prof. Dr. Xiaoshuang Shi
Guest Editor
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A predictive analytics framework for infant mortality rate estimation using a smart healthcare system
Authors: Ali Fida; Muhammad Usman; Tazeen Athar; Alvis Fong
Affiliation: Western Michigan University, Kalamazoo, United States
Abstract: Accurate and timely prediction of vaccine-preventable diseases is always a significant public health matter to reduce child mortality. Pakistan has nationwide different programs for timely treatment of vaccine-preventable diseases, but unluckily, coverage is quite low in spite of the accessibility of free vaccination, and it influences the infant mortality rate (IMR). It’s crucial for decision-makers to design effective strategies timely for a reduction in IMR rate. In terms of prediction model building, the healthcare program datasets have never been integrated in the past to know the variables influencing IMR across various healthcare programs. Most statistical-based methods are implemented in different fields of life but without augmentation of basic domain knowledge, cause tendency, and yield erroneous results. In order to address the aforementioned limitations, an automated and accurate prediction methodology has been proposed in this work, which effectively overcomes the said limitations. The proposed methodology has been validated through an integrated approach by applying different machine learning algorithms. Experimental results have been conducted IMR on datasets taken from Sindh healthcare programs, and supremacy of the proposed methodology has been observed by accurate prediction. Furthermore, comparative analysis based on five regression algorithms shows that fine-tuned Random Forest-based Regressor is performing well compared to other regression algorithms.