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Diagnostics
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Artificial Intelligence in Brain Imaging
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Artificial Intelligence in Brain Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8300

Special Issue Editors

Dr. Jonathan Frederik Carlsen

E-Mail Website
Guest Editor
Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
Interests: neuroradiology; MRI; computed tomography; brain tumor imaging; artificial intelligence; deep learning; stroke imaging; idiopathic normal pressure hydrocephalus
Special Issues, Collections and Topics in MDPI journals
Dr. Claes Nøhr Ladefoged

E-Mail Website
Guest Editor
Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
Interests: neuroradiology; MRI; computed tomography; PET; artificial intelligence; deep learning; cancer; dementia; decision-support

Special Issue Information

Dear Colleagues, 

Artificial intelligence (AI) is becoming an integrated part of the neuroradiological workflow from patient referral, through image acquisition, to image prioritization, interpretation, and reporting. The application of AI algorithms in neuroradiology poses new challenges and opportunities for radiologists and other neuroimaging specialists and data scientists alike and necessitates new collaborations between these professions. With this Special Issue of Diagnostics, we want to provide a platform for publications hailing from such academic partnerships. We welcome the submission of papers presenting knowledge that will help to advance and evaluate artificial intelligence for clinical neuroimaging for any purpose. All publications concerning artificial intelligence applied to in vivo brain imaging including X-ray, digital subtraction angiography, computed tomography X-ray, magnetic resonance imaging, positron emission tomography, and single-photon emission computed tomography are within the scope of this Special Issue. Further, manuscripts on imaging of all brain pathologies including trauma, brain tumors, stroke, and degenerative and inflammatory diseases, and at all time points from diagnosis to treatment evaluation, are welcomed.  

Dr. Jonathan Frederik Carlsen
Dr. Claes Nøhr Ladefoged
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. Diagnostics 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

  • neuroimaging
  • artificial intelligence
  • MRI
  • deep learning
  • machine learning
  • computed tomography
  • SPECT
  • PET
  • brain tumors
  • stroke
  • dementia
  • multiple sclerosis

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Published Papers (2 papers)

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Research

20 pages, 720 KiB  
Article
An Efficient Ensemble Approach for Alzheimer’s Disease Detection Using an Adaptive Synthetic Technique and Deep Learning
by Muhammad Mujahid, Amjad Rehman, Teg Alam, Faten S. Alamri, Suliman Mohamed Fati and Tanzila Saba
Diagnostics 2023, 13(15), 2489; https://doi.org/10.3390/diagnostics13152489 - 26 Jul 2023
Cited by 22 | Viewed by 3352
Abstract
Alzheimer’s disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer’s disease is more important due to the shortage of expert medical staff, because it reduces [...] Read more.
Alzheimer’s disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer’s disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer’s disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer’s disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods. Full article
(This article belongs to the Special Issue Artificial Intelligence in Brain Imaging)
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17 pages, 2311 KiB  
Article
Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans
by Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, Unza Shakeel, Muhammad Irfan, Khlood M. Mehdar, Hanan Talal Halawani, Ali H. Alghamdi, Abdullah Fahad A. Alshamrani and Samar M. Alqhtani
Diagnostics 2023, 13(12), 2094; https://doi.org/10.3390/diagnostics13122094 - 16 Jun 2023
Cited by 9 | Viewed by 3912
Abstract
A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. [...] Read more.
A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability. Full article
(This article belongs to the Special Issue Artificial Intelligence in Brain Imaging)
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