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J. Imaging
Special Issues
Internet-of-Medical-Things-Streamed Medical-Image-Based Recommendation and Optimization Techniques Using Federated Learning
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Internet-of-Medical-Things-Streamed Medical-Image-Based Recommendation and Optimization Techniques Using Federated Learning

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 5628

Special Issue Editor

Dr. Saurav Mallik

E-Mail Website
Guest Editor
Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
Interests: computational biology; medical imaging; IoT; data mining

Special Issue Information

Dear Colleagues,

Nowadays, the use of Internet of Medical Things (IoMT) devices has the great potential to revolutionize the biomedical and healthcare services by generating a huge amount of data (particularly, medical images) that can be utilized to enhance patients’ outcomes and further decision making. IoMT devices include a wide range of connected medical devices such as wearable sensors, implantable devices, and mobile apps that can easily collect patient data in real-time scenarios. However, the utilization of the IoMT devices also raises several concerns about the security and privacy of the patients’ medical imaging data. Federated Learning (FL) is a distributed machine learning technique that basically allows multiple IoMT devices to collaborate and learn from each other's data while keeping the sensitive patients’ data secure. FL enables the machine learning models to be trained across multiple devices without the necessity to transfer the data to a central server/hub, thereby reducing the risk of data breaches and protecting the patients’ privacy.

FL-based IoMT has the great efficiency to transform healthcare services by enabling personalized healthcare delivery, chronic disease management, and the early detection of infectious diseases from medical image datasets. Personalized medication recommendations for chronic diseases can be generated through analyzing data from multiple IoMT devices and tailoring the treatment plans to individual patients. The early detection of infectious diseases can be achieved by analyzing real-time data from the connected devices and identifying outbreaks before they become widespread. This Special Issue invites review and research papers on IoMT-based medical image streaming, recommendation, and optimization techniques using FL to improve healthcare delivery and patient outcomes. Topics of interest include personalized medication recommendations for chronic diseases, the early detection of infectious diseases, secure FL-based healthcare data analysis, FL-based optimization of medical image analysis, FL-based chronic disease management, and privacy and security concerns in FL-based IoMT.

We encourage authors to submit original research articles, reviews, and case studies on IoMT-based recommendation and optimization techniques via FL. All submissions will be peer reviewed to ensure the quality and relevance of the articles. We hope that this Special Issue will contribute to the advancement of IoMT-based healthcare and encourage the adoption of FL in healthcare to enhance patients’ outcomes.

The current Special Issue will cover a wide range, including the following topics associated with IOMT and medical imaging, but is not limited to these only:

  • Personalized medication recommendations for chronic diseases using medical image processing.
  • MRI/CT-based early detection of infectious diseases.
  • Secure FL-based biomedical and healthcare data analysis.
  • FL-based optimization of medical image analysis.
  • FL-based medical image retrieval techniques for chronic disease management.
  • Privacy and security issues in IoMT.
  • FL-based prediction and prevention of cancer and various neurodegenerative diseases.
  • Secure and efficient FL-based IoMT systems for resource-constrained devices.
  • Privacy-preserving FL-based IoMT systems via differential privacy or other privacy-enhancing methodologies.
  • FL-based IoMT for personalized nutrition and dietary recommendations.
  • FL-based IoMT for mental health monitoring and treatment.
  • FL-based IoMT for the early detection and management of cardiovascular diseases from CT/MRI/Xray image datasets.
  • FL-based IoMT for real-time image monitoring and management of sleep disorders.
  • FL-based IoMT for personalized rehabilitation as well as physical therapy.
  • FL-based IoMT for detecting adverse drug events and improving medication safety.
  • FL-based IoMT for enhancing clinical decision making and patients’ outcomes.

Dr. Saurav Mallik
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoMT
  • medical imaging
  • optimization
  • federal learning
  • recommendation system

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Published Papers (1 paper)

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16 pages, 3949 KiB  
Article
A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms
by Rohit Sharma, Gautam Kumar Mahanti, Ganapati Panda, Adyasha Rath, Sujata Dash, Saurav Mallik and Ruifeng Hu
J. Imaging 2023, 9(9), 173; https://doi.org/10.3390/jimaging9090173 - 27 Aug 2023
Cited by 20 | Viewed by 5025
Abstract
Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, [...] Read more.
Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity. Full article
(This article belongs to the Special Issue Internet-of-Medical-Things-Streamed Medical-Image-Based Recommendation and Optimization Techniques Using Federated Learning)
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