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Mathematics
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Medical Imaging Analysis with Artificial Intelligence
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Medical Imaging Analysis with Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 6077

Special Issue Editors

Dr. Gourav Modanwal

E-Mail Website
Guest Editor
Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech, Atlanta, GA 30332, USA
Interests: medical image processing; human-computer interaction; pattern recognition; computer-aided diagnosis (CAD) and radiomics
Dr. Rakesh Shiradkar

E-Mail Website
Guest Editor
Wallace H. Coulter Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
Interests: image processing; computer vision; machine learning; medical image analysis; radiomics

Special Issue Information

Dear Colleagues,

Medical imaging analysis is a vital part of modern healthcare, helping clinicians to diagnose and monitor a wide range of diseases and conditions. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of powerful tools and techniques for analyzing medical images, with the potential to improve diagnostic accuracy and patient outcomes. This Special Issue aims to showcase the latest research and developments in medical imaging analysis with AI, and to explore the challenges and opportunities in this exciting field.

Topics:

We invite submissions on all aspects of medical imaging analysis with AI, including but not limited to:

  • Radiomics and image-based biomarkers
  • Deep learning for medical image analysis
  • Computer-aided diagnosis (CAD) systems
  • Image segmentation and registration
  • Convolutional neural networks (CNN) for medical image classification and segmentation
  • Multi-modal medical image analysis
  • Medical data mining and analysis
  • Real-time medical imaging analysis
  • Clinical applications of medical imaging analysis with AI

Submission Guidelines:

Authors are invited to submit original research papers, review articles, or short communications. All submissions will undergo rigorous peer-review by experts in the field. Manuscripts must be prepared according to the journal's guidelines and should be submitted online via the journal's submission system. The deadline for submission is 29 Feb 2024. Accepted papers will be published in the Special Issue of the journal.

Dr. Gourav Modanwal
Dr. Rakesh Shiradkar
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • radiomics
  • deep learning
  • computer-aided diagnosis (CAD)
  • image segmentation
  • convolutional neural networks (CNN)
  • machine learning
  • medical image processing
  • diagnostic imaging
  • medical data analysis
  • image-based biomarkers

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

26 pages, 1159 KiB  
Article
FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation
by Chao Nie, Chao Xu and Zhengping Li
Mathematics 2024, 12(22), 3580; https://doi.org/10.3390/math12223580 - 15 Nov 2024
Viewed by 381
Abstract
The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to [...] Read more.
The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to effectively capture global and remote semantic features, preserve more local detail information, and provide clearer and more precise boundaries. Specifically, first, we use PVT v2 backbone to learn multi-scale global feature representations to adapt to changes in bladder tumor size and shape. Secondly, we propose a new feature exploration attention module (FEA) to fully explore the potential local detail information in the shallow features extracted by the PVT v2 backbone, eliminate noise, and supplement the missing fine-grained details for subsequent decoding stages. At the same time, we propose a new boundary enhancement and refinement module (BER), which generates high-quality boundary clues through boundary detection operators to help the decoder more effectively preserve the boundary features of bladder tumors and refine and adjust the final predicted feature map. Then, we propose a new efficient self-attention calibration decoder module (ESCD), which, with the help of boundary clues provided by the BER module, gradually and effectively recovers global contextual information and local detail information from high-level features after calibration enhancement and low-level features after exploration attention. Extensive experiments on the cystoscopy dataset BtAMU and five colonoscopy datasets have shown that FEBE-Net outperforms 11 state-of-the-art (SOTA) networks in segmentation performance, with higher accuracy, stronger robust stability, and generalization ability. Full article
(This article belongs to the Special Issue Medical Imaging Analysis with Artificial Intelligence)
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35 pages, 11322 KiB  
Article
SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images
by Ahmad Naeem, Tayyaba Anees, Mudassir Khalil, Kiran Zahra, Rizwan Ali Naqvi and Seung-Won Lee
Mathematics 2024, 12(7), 1030; https://doi.org/10.3390/math12071030 - 29 Mar 2024
Cited by 10 | Viewed by 3825
Abstract
The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin [...] Read more.
The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection. Full article
(This article belongs to the Special Issue Medical Imaging Analysis with Artificial Intelligence)
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17 pages, 852 KiB  
Article
A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis
by Jishu K. Medhi, Pusheng Ren, Mengsha Hu and Xuhui Chen
Mathematics 2023, 11(22), 4566; https://doi.org/10.3390/math11224566 - 7 Nov 2023
Cited by 1 | Viewed by 1300
Abstract
Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data without substantial domain knowledge. However, training a deep neural network requires a vast amount of labeled samples. Additionally, a well-trained model may be [...] Read more.
Deep learning is a promising technique for bioelectrical signal analysis, as it can automatically discover hidden features from raw data without substantial domain knowledge. However, training a deep neural network requires a vast amount of labeled samples. Additionally, a well-trained model may be sensitive to the study object, and its performance may deteriorate sharply when transferred to other study objects. We propose a deep multi-task learning approach for bioelectrical signal analysis to address these issues. Explicitly, we define two distinct scenarios, the consistent source-target scenario and the inconsistent source-target scenario based on the motivation and purpose of the tasks. For each scenario, we present methods to decompose the original task and dataset into multiple subtasks and sub-datasets. Correspondingly, we design the generic deep parameter-sharing neural networks to solve the multi-task learning problem and illustrate the details of implementation with one-dimension convolutional neural networks (1D CNN), vanilla recurrent neural networks (RNN), recurrent neural networks with long short-term memory units (LSTM), and recurrent neural networks with gated recurrent units (GRU). In these two scenarios, we conducted extensive experiments on four electrocardiogram (ECG) databases. The results demonstrate the benefits of our approach, showing that our proposed method can improve the accuracy of ECG data analysis (up to 5.2%) in the MIT-BIH arrhythmia database. Full article
(This article belongs to the Special Issue Medical Imaging Analysis with Artificial Intelligence)
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