Artificial Intelligence in Biomedical Image Analysis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 13810

Special Issue Editors

Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
Interests: biosignaling; bioimaging modeling and computer-assisted functional diagnostic diagnosis systems, including those using CT, MRI, EMG, ECG, EEG, and other physiological signals
Special Issues, Collections and Topics in MDPI journals
1. Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA 95661, USA
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The word “smart” that we use frequently in our daily life is usually associated with some kind of machine learning. Whether we are talking about a smart device or a smart app, machine learning is playing a key role in the background. How smart the app or the device is, depends on the selection of the right machine learning technique that will grant the best interpretation of the data and signals involved to achieve the desired outcome. This is mainly why machine learning techniques are progressing rapidly and attracting the attention of researchers and investors, especially in the medical field.

This special issue will focus on utilizing machine learning in medicine. Researchers are encouraged to submit original research articles or review articles discussing the state-of-the-art machine learning techniques for medical applications including, but not limited to, the early diagnosis of various kinds of cancer and neurological disorders. The articles are expected to provide an extensive description of the machine learning technique(s) involved as well as the medical application,  highlighting the performance evaluation metrics used.

Prof. Dr. Ayman El-Baz
Dr. Jasjit S. Suri
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

  • artificial intelligence
  • biomedical image analysis
  • diagnosis
  • cancer
  • neurological disorders

Related Special Issue

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 4113 KiB  
Article
Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data
by André Sobiecki, Lubomir M. Hadjiiski, Heang-Ping Chan, Ravi K. Samala, Chuan Zhou, Jadranka Stojanovska and Prachi P. Agarwal
Diagnostics 2024, 14(3), 341; https://doi.org/10.3390/diagnostics14030341 - 05 Feb 2024
Viewed by 629
Abstract
The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk for the patient and requires prompt treatment with oxygen therapy and hospitalization while those with less severe lung infection often stay on observation. Also, severe infections are more [...] Read more.
The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk for the patient and requires prompt treatment with oxygen therapy and hospitalization while those with less severe lung infection often stay on observation. Also, severe infections are more likely to have long-standing residual changes in their lungs and may need follow-up imaging. We have developed deep learning neural network models for classifying severe vs. non-severe lung infections in COVID-19 patients on chest radiographs (CXR). A deep learning U-Net model was developed to segment the lungs. Inception-v1 and Inception-v4 models were trained for the classification of severe vs. non-severe COVID-19 infection. Four CXR datasets from multi-country and multi-institutional sources were used to develop and evaluate the models. The combined dataset consisted of 5748 cases and 6193 CXR images with physicians’ severity ratings as reference standard. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. We studied the reproducibility of classification performance using the different combinations of training and validation data sets. We also evaluated the generalizability of the trained deep learning models using both independent internal and external test sets. The Inception-v1 based models achieved AUC ranging between 0.81 ± 0.02 and 0.84 ± 0.0, while the Inception-v4 models achieved AUC in the range of 0.85 ± 0.06 and 0.89 ± 0.01, on the independent test sets, respectively. These results demonstrate the promise of using deep learning models in differentiating COVID-19 patients with severe from non-severe lung infection on chest radiographs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

19 pages, 23995 KiB  
Article
Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images
by Toan Duc Nguyen, Duc-Tai Le, Junghyun Bum, Seongho Kim, Su Jeong Song and Hyunseung Choo
Diagnostics 2024, 14(1), 105; https://doi.org/10.3390/diagnostics14010105 - 03 Jan 2024
Cited by 1 | Viewed by 932
Abstract
Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting [...] Read more.
Ultra-wide-field fundus imaging (UFI) provides comprehensive visualization of crucial eye components, including the optic disk, fovea, and macula. This in-depth view facilitates doctors in accurately diagnosing diseases and recommending suitable treatments. This study investigated the application of various deep learning models for detecting eye diseases using UFI. We developed an automated system that processes and enhances a dataset of 4697 images. Our approach involves brightness and contrast enhancement, followed by applying feature extraction, data augmentation and image classification, integrated with convolutional neural networks. These networks utilize layer-wise feature extraction and transfer learning from pre-trained models to accurately represent and analyze medical images. Among the five evaluated models, including ResNet152, Vision Transformer, InceptionResNetV2, RegNet and ConVNext, ResNet152 is the most effective, achieving a testing area under the curve (AUC) score of 96.47% (with a 95% confidence interval (CI) of 0.931–0.974). Additionally, the paper presents visualizations of the model’s predictions, including confidence scores and heatmaps that highlight the model’s focal points—particularly where lesions due to damage are evident. By streamlining the diagnosis process and providing intricate prediction details without human intervention, our system serves as a pivotal tool for ophthalmologists. This research underscores the compatibility and potential of utilizing ultra-wide-field images in conjunction with deep learning. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

29 pages, 9875 KiB  
Article
Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer
by Mohammed Hamdi, Ebrahim Mohammed Senan, Bakri Awaji, Fekry Olayah, Mukti E. Jadhav and Khaled M. Alalayah
Diagnostics 2023, 13(15), 2538; https://doi.org/10.3390/diagnostics13152538 - 31 Jul 2023
Cited by 2 | Viewed by 1262
Abstract
Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it [...] Read more.
Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems’ performance that the DL models’ combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

14 pages, 2238 KiB  
Article
Development and Validation of Machine Learning Models to Classify Artery Stenosis for Automated Generating Ultrasound Report
by Chih-Yang Yeh, Hsun-Hua Lee, Md. Mohaimenul Islam, Chiu-Hui Chien, Suleman Atique, Lung Chan and Ming-Chin Lin
Diagnostics 2022, 12(12), 3047; https://doi.org/10.3390/diagnostics12123047 - 05 Dec 2022
Cited by 1 | Viewed by 1352
Abstract
Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, [...] Read more.
Duplex ultrasonography (DUS) is a safe, non-invasive, and affordable primary screening tool to identify the vascular risk factors of stroke. The overall process of DUS examination involves a series of complex processes, such as identifying blood vessels, capturing the images of blood vessels, measuring the velocity of blood flow, and then physicians, according to the above information, determining the severity of artery stenosis for generating final ultrasound reports. Generation of transcranial doppler (TCD) and extracranial carotid doppler (ECCD) ultrasound reports involves a lot of manual review processes, which is time-consuming and makes it easy to make errors. Accurate classification of the severity of artery stenosis can provide an early opportunity for decision-making regarding the treatment of artery stenosis. Therefore, machine learning models were developed and validated for classifying artery stenosis severity based on hemodynamic features. This study collected data from all available cases and controlled at one academic teaching hospital in Taiwan between 1 June 2020, and 30 June 2020, from a university teaching hospital and reviewed all patients’ medical records. Supervised machine learning models were developed to classify the severity of artery stenosis. The receiver operating characteristic curve, accuracy, sensitivity, specificity, and positive and negative predictive value were used for model performance evaluation. The performance of the random forest model was better compared to the logistic regression model. For ECCD reports, the accuracy of the random forest model to predict stenosis in various sites was between 0.85 and 1. For TCD reports, the overall accuracy of the random forest model to predict stenosis in various sites was between 0.67 and 0.86. The findings of our study suggest that a machine learning-based model accurately classifies artery stenosis, which indicates that the model has enormous potential to facilitate screening for artery stenosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

23 pages, 5355 KiB  
Article
A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods
by Omneya Attallah, Muhammet Fatih Aslan and Kadir Sabanci
Diagnostics 2022, 12(12), 2926; https://doi.org/10.3390/diagnostics12122926 - 23 Nov 2022
Cited by 19 | Viewed by 2450
Abstract
Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will [...] Read more.
Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

15 pages, 2283 KiB  
Article
Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears
by Dilber Uzun Ozsahin, Mubarak Taiwo Mustapha, Basil Bartholomew Duwa and Ilker Ozsahin
Diagnostics 2022, 12(11), 2702; https://doi.org/10.3390/diagnostics12112702 - 05 Nov 2022
Cited by 10 | Viewed by 2278
Abstract
Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving [...] Read more.
Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

Review

Jump to: Research

21 pages, 813 KiB  
Review
Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions
by Tarig Elhakim, Kelly Trinh, Arian Mansur, Christopher Bridge and Dania Daye
Diagnostics 2023, 13(5), 968; https://doi.org/10.3390/diagnostics13050968 - 03 Mar 2023
Cited by 2 | Viewed by 1796
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting [...] Read more.
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

24 pages, 1392 KiB  
Review
Preliminary Stages for COVID-19 Detection Using Image Processing
by Taqwa Ahmed Alhaj, Inshirah Idris, Fatin A. Elhaj, Tusneem A. Elhassan, Muhammad Akmal Remli, Maheyzah Md Siraj and Mohd Shafry Mohd Rahim
Diagnostics 2022, 12(12), 3171; https://doi.org/10.3390/diagnostics12123171 - 15 Dec 2022
Cited by 2 | Viewed by 1982
Abstract
COVID-19 was first discovered in December 2019 in Wuhan. There have been reports of thousands of illnesses and hundreds of deaths in almost every region of the world. Medical images, when combined with cutting-edge technology such as artificial intelligence, have the potential to [...] Read more.
COVID-19 was first discovered in December 2019 in Wuhan. There have been reports of thousands of illnesses and hundreds of deaths in almost every region of the world. Medical images, when combined with cutting-edge technology such as artificial intelligence, have the potential to improve the efficiency of the public health system and deliver faster and more reliable findings in the detection of COVID-19. The process of developing the COVID-19 diagnostic system begins with image accusation and proceeds via preprocessing, feature extraction, and classification. According to literature review, several attempts to develop taxonomies for COVID-19 detection using image processing methods have been introduced. However, most of these adhere to a standard category that exclusively considers classification methods. Therefore, in this study a new taxonomy for the early stages of COVID-19 detection is proposed. It attempts to offer a full grasp of image processing in COVID-19 while considering all phases required prior to classification. The survey concludes with a discussion of outstanding concerns and future directions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Image Analysis)
Show Figures

Figure 1

Back to TopTop