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Advances in Medical Image Analysis and Computer-Aided Diagnosis
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Advances in Medical Image Analysis and Computer-Aided Diagnosis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 28018

Special Issue Editor

Dr. Valentin Sebastian Schäfer

E-Mail Website
Guest Editor
Associate Professor of Internal Medicine, Head of the Department of Rheumatology and Clinical Immunology, Head of the Ultrasound Diagnostic Centre, Speaker of the Centre for Rare Rheumatological Diseases, Department of Internal Medicine IIIOncology, Hematology, Rheumatology and Clinical ImmunologyBuilding 20 – Zentrum für Integrative Medizin, University Hospital BonnVenusberg Campus, 153127 Bonn, Germany
Interests: large vessel vasculitis; imaging in rheumatic disease; modern concepts for early diagnosis of psoriatic arthritis; pulmonary diseases in arthritides; teaching study development in ultrasound; effect of sports on the musculoskeletal system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical image analysis aims to increase the efficiency of clinical examination and medical intervention. Medical imaging refers to several different technologies that are used to view the human body in order to diagnose, monitor, or treat medical conditions. Each type of technology gives different information about the area of the body being studied or treated, related to possible disease, injury, or the effectiveness of medical treatment.
Computer-aided diagnosis is an interdisciplinary effort that joins radiological and pathology image processing with computer vision and artificial intelligence in order to identify disease.

The objective of this Special Issue is to generate a comprehensive understanding of medical AI and biosensors in clinical applications. It will also highlight recent advances in the diverse implementations in healthcare management and monitoring. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in this field.

Dr. Valentin Sebastian Schäfer
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. Applied Sciences 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 2400 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

  • biomedical image processing
  • computer-aided diagnosis
  • artificial intelligence systems
  • computer-aided detection
  • imaging biomarkers
  • machine and deep learning for biomedical imaging
  • computational medical imaging

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Published Papers (5 papers)

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Research

19 pages, 7374 KiB  
Article
Node2Node: Self-Supervised Cardiac Diffusion Tensor Image Denoising Method
by Hongbo Du, Nannan Yuan and Lihui Wang
Appl. Sci. 2023, 13(19), 10829; https://doi.org/10.3390/app131910829 - 29 Sep 2023
Cited by 1 | Viewed by 1376
Abstract
Although the existing cardiac diffusion tensor imaging (DTI) denoising methods have achieved promising results, most of them are dependent on the number of diffusion gradient directions, noise distributions, and noise levels. To address these issues, we propose a novel self-supervised cardiac DTI denoising [...] Read more.
Although the existing cardiac diffusion tensor imaging (DTI) denoising methods have achieved promising results, most of them are dependent on the number of diffusion gradient directions, noise distributions, and noise levels. To address these issues, we propose a novel self-supervised cardiac DTI denoising network, Node2Node, which firstly expresses the diffusion-weighted (DW) image volumes along different directions as a graph, then the graph framelet transform (GFT) is implemented to map the DW signals into the GFT coefficients at different spectral bands, allowing us to accurately match the DW image pairs. After that, using the matched image pairs as input and target, a ResNet-like network is used to denoise in a self-supervised manner. In addition, a novel edge-aware loss based on pooling operation is proposed to retain the edge. Through comparison with several state-of-the-art methods on synthetic, ex vivo porcine, and in vivo human cardiac DTI datasets, we showed that the root mean square error (RMSE) of DW images and the average angular error (AAE) of fiber orientations obtained using Node2Node are the smallest, improved by 47.5% and 23.7%, respectively, on the synthetic dataset, demonstrating that Node2Node is not sensitive to the properties of the dataset. Full article
(This article belongs to the Special Issue Advances in Medical Image Analysis and Computer-Aided Diagnosis)
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11 pages, 1923 KiB  
Article
Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
by Adham Aleid, Khalid Alhussaini, Reem Alanazi, Meaad Altwaimi, Omar Altwijri and Ali S. Saad
Appl. Sci. 2023, 13(6), 3808; https://doi.org/10.3390/app13063808 - 16 Mar 2023
Cited by 27 | Viewed by 8048
Abstract
Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial [...] Read more.
Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management. Full article
(This article belongs to the Special Issue Advances in Medical Image Analysis and Computer-Aided Diagnosis)
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19 pages, 816 KiB  
Article
Explainable-AI in Automated Medical Report Generation Using Chest X-ray Images
by Saad Bin Ahmed, Roberto Solis-Oba and Lucian Ilie
Appl. Sci. 2022, 12(22), 11750; https://doi.org/10.3390/app122211750 - 18 Nov 2022
Cited by 13 | Viewed by 8375
Abstract
The use of machine learning in healthcare has the potential to revolutionize virtually every aspect of the industry. However, the lack of transparency in AI applications may lead to the problem of trustworthiness and reliability of the information provided by these applications. Medical [...] Read more.
The use of machine learning in healthcare has the potential to revolutionize virtually every aspect of the industry. However, the lack of transparency in AI applications may lead to the problem of trustworthiness and reliability of the information provided by these applications. Medical practitioners rely on such systems for clinical decision making, but without adequate explanations, diagnosis made by these systems cannot be completely trusted. Explainability in Artificial Intelligence (XAI) aims to improve our understanding of why a given output has been produced by an AI system. Automated medical report generation is one area that would benefit greatly from XAI. This survey provides an extensive literature review on XAI techniques used in medical image analysis and automated medical report generation. We present a systematic classification of XAI techniques used in this field, highlighting the most important features of each one that could be used by future research to select the most appropriate XAI technique to create understandable and reliable explanations for decisions made by AI systems. In addition to providing an overview of the state of the art in this area, we identify some of the most important issues that need to be addressed and on which research should be focused. Full article
(This article belongs to the Special Issue Advances in Medical Image Analysis and Computer-Aided Diagnosis)
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13 pages, 2498 KiB  
Article
An Efficient Deep Learning Approach for Colon Cancer Detection
by Ahmed S. Sakr, Naglaa F. Soliman, Mehdhar S. Al-Gaashani, Paweł Pławiak, Abdelhamied A. Ateya and Mohamed Hammad
Appl. Sci. 2022, 12(17), 8450; https://doi.org/10.3390/app12178450 - 24 Aug 2022
Cited by 57 | Viewed by 5156
Abstract
Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose [...] Read more.
Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient. Full article
(This article belongs to the Special Issue Advances in Medical Image Analysis and Computer-Aided Diagnosis)
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17 pages, 1107 KiB  
Article
Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
by Hyunkyung Shin, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh and Honguk Woo
Appl. Sci. 2022, 12(14), 7317; https://doi.org/10.3390/app12147317 - 21 Jul 2022
Cited by 6 | Viewed by 4154
Abstract
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there [...] Read more.
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model training. Thus, there is a need for sample-efficient learning techniques, particularly in the field of medical image analysis, as significant cost and effort are required to obtain a sufficient number of well-annotated high-quality training samples. In this paper, we address the problem of deep neural network training under sample deficiency by investigating several sample-efficient deep learning techniques. We concentrate on applying these techniques to skin burn image analysis and classification. We first build a large-scale, professionally annotated dataset of skin burn images, which enables the establishment of convolutional neural network (CNN) models for burn severity assessment with high accuracy. We then deliberately set data limitation conditions and adapt several sample-efficient techniques, such as transferable learning (TL), self-supervised learning (SSL), federated learning (FL), and generative adversarial network (GAN)-based data augmentation, to those conditions. Through comprehensive experimentation, we evaluate the sample-efficient deep learning techniques for burn severity assessment, and show, in particular, that SSL models learned on a small task-specific dataset can achieve comparable accuracy to a baseline model learned on a six-times larger dataset. We also demonstrate the applicability of FL and GANs to model training under different data limitation conditions that commonly occur in the area of healthcare and medicine where deep learning models are adopted. Full article
(This article belongs to the Special Issue Advances in Medical Image Analysis and Computer-Aided Diagnosis)
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