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Diagnostics
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Lesion Detection and Analysis Using Artificial Intelligence
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Lesion Detection and Analysis Using Artificial Intelligence

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 (31 December 2022) | Viewed by 18248

Special Issue Editors

Prof. Dr. Luca Saba

E-Mail Website
Guest Editor
Department of Radiology, University of Cagliari, 09042 Cagliari, Italy
Interests: neuroradiology; vascular imaging; cardiovascular imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Jasjit S. Suri

E-Mail Website
Guest Editor
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, 

Artificial intelligence (AI), including deep learning and machine learning, is currently undergoing rapid development, having garnered substantial public attention in recent years. This Special Issue plans to focus on topics and issues regarding the development AI to become more meaningfully intelligent for lesion detection and analysis, scientific validations of AI systems, clinical evaluations of AI systems, bias detection in AI systems, high-speed AI systems, and edge-devices for AI systems, all these facets of AI enveloping different branches of medicine and leading to personalized and precision medicine.

Prof. Dr. Luca Saba
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
  • deep learning
  • machine learning
  • lesion detection and analysis
  • diagnosis

Related Special Issue

Published Papers (6 papers)

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Research

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15 pages, 3426 KiB  
Article
A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images
by Fayadh Alenezi, Ammar Armghan and Kemal Polat
Diagnostics 2023, 13(2), 262; https://doi.org/10.3390/diagnostics13020262 - 10 Jan 2023
Cited by 10 | Viewed by 2894
Abstract
Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this [...] Read more.
Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
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19 pages, 7920 KiB  
Article
GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification
by Muhammad Attique Khan, Naveera Sahar, Wazir Zada Khan, Majed Alhaisoni, Usman Tariq, Muhammad H. Zayyan, Ye Jin Kim and Byoungchol Chang
Diagnostics 2022, 12(11), 2718; https://doi.org/10.3390/diagnostics12112718 - 07 Nov 2022
Cited by 15 | Viewed by 2251
Abstract
In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases [...] Read more.
In the last few years, artificial intelligence has shown a lot of promise in the medical domain for the diagnosis and classification of human infections. Several computerized techniques based on artificial intelligence (AI) have been introduced in the literature for gastrointestinal (GIT) diseases such as ulcer, bleeding, polyp, and a few others. Manual diagnosis of these infections is time consuming, expensive, and always requires an expert. As a result, computerized methods that can assist doctors as a second opinion in clinics are widely required. The key challenges of a computerized technique are accurate infected region segmentation because each infected region has a change of shape and location. Moreover, the inaccurate segmentation affects the accurate feature extraction that later impacts the classification accuracy. In this paper, we proposed an automated framework for GIT disease segmentation and classification based on deep saliency maps and Bayesian optimal deep learning feature selection. The proposed framework is made up of a few key steps, from preprocessing to classification. Original images are improved in the preprocessing step by employing a proposed contrast enhancement technique. In the following step, we proposed a deep saliency map for segmenting infected regions. The segmented regions are then used to train a pre-trained fine-tuned model called MobileNet-V2 using transfer learning. The fine-tuned model’s hyperparameters were initialized using Bayesian optimization (BO). The average pooling layer is then used to extract features. However, several redundant features are discovered during the analysis phase and must be removed. As a result, we proposed a hybrid whale optimization algorithm for selecting the best features. Finally, the selected features are classified using an extreme learning machine classifier. The experiment was carried out on three datasets: Kvasir 1, Kvasir 2, and CUI Wah. The proposed framework achieved accuracy of 98.20, 98.02, and 99.61% on these three datasets, respectively. When compared to other methods, the proposed framework shows an improvement in accuracy. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
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23 pages, 9001 KiB  
Article
Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches
by Suliman Mohamed Fati, Ebrahim Mohammed Senan and Yasir Javed
Diagnostics 2022, 12(8), 1899; https://doi.org/10.3390/diagnostics12081899 - 05 Aug 2022
Cited by 19 | Viewed by 2208
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long [...] Read more.
Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
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12 pages, 4549 KiB  
Article
Feasibility of Differential Dose—Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence
by Yoshiyuki Katsuta, Noriyuki Kadoya, Yuto Sugai, Yu Katagiri, Takaya Yamamoto, Kazuya Takeda, Shohei Tanaka and Keiichi Jingu
Diagnostics 2022, 12(6), 1354; https://doi.org/10.3390/diagnostics12061354 - 31 May 2022
Cited by 1 | Viewed by 1645
Abstract
The purpose of this study is to introduce differential dose–volume histogram (dDVH) features into machine learning for radiation pneumonitis (RP) prediction and to demonstrate the predictive performance of the developed model based on integrated cumulative dose–volume histogram (cDVH) and dDVH features. Materials and [...] Read more.
The purpose of this study is to introduce differential dose–volume histogram (dDVH) features into machine learning for radiation pneumonitis (RP) prediction and to demonstrate the predictive performance of the developed model based on integrated cumulative dose–volume histogram (cDVH) and dDVH features. Materials and methods: cDVH and dDVH features were calculated for 153 patients treated for non-small-cell lung cancer with 60–66 Gy and dose bins ranging from 2 to 8 Gy in 2 Gy increments. RP prediction models were developed with the least absolute shrinkage and selection operator (LASSO) through fivefold cross-validation. Results: Among the 152 patients in the patient cohort, 41 presented ≥grade 2 RP. The interdependencies between cDVH features evaluated by Spearman’s correlation were significantly resolved by the inclusion of dDVH features. The average area under curve for the RP prediction model using cDVH and dDVH model was 0.73, which was higher than the average area under curve using cDVH model for 0.62 with statistically significance (p < 0.01). An analysis using the entire set of regression coefficients determined by LASSO demonstrated that dDVH features represented four of the top five frequently selected features in the model fitting, regardless of dose bin. Conclusions: We successfully developed an RP prediction model that integrated cDVH and dDVH features. The best RP prediction model was achieved using dDVH (dose bin = 4 Gy) features in the machine learning process. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
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18 pages, 6384 KiB  
Article
An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images
by Ankit Kurmi, Shreya Biswas, Shibaprasad Sen, Aleksandr Sinitca, Dmitrii Kaplun and Ram Sarkar
Diagnostics 2022, 12(5), 1173; https://doi.org/10.3390/diagnostics12051173 - 08 May 2022
Cited by 17 | Viewed by 3686
Abstract
Parkinson’s Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of dopamine-producing neurons. [...] Read more.
Parkinson’s Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of dopamine-producing neurons. Tremors in hands is usually the initial symptom, followed by rigidity, bradykinesia, postural instability, and impaired balance. Proper diagnosis and preventive treatment can help patients improve their quality of life. We have proposed an ensemble of Deep Learning (DL) models to predict Parkinson’s using DaTscan images. Initially, we have used four DL models, namely, VGG16, ResNet50, Inception-V3, and Xception, to classify Parkinson’s disease. In the next stage, we have applied a Fuzzy Fusion logic-based ensemble approach to enhance the overall result of the classification model. The proposed model is assessed on a publicly available database provided by the Parkinson’s Progression Markers Initiative (PPMI). The achieved recognition accuracy, Precision, Sensitivity, Specificity, F1-score from the proposed model are 98.45%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively which are higher than the individual model. We have also developed a Graphical User Interface (GUI)-based software tool for public use that instantly detects all classes using Magnetic Resonance Imaging (MRI) with reasonable accuracy. The proposed method offers better performance compared to other state-of-the-art methods in detecting PD. The developed GUI-based software tool can play a significant role in detecting the disease in real-time. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
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Review

Jump to: Research

47 pages, 8936 KiB  
Review
A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review
by Jasjit S. Suri, Mrinalini Bhagawati, Sudip Paul, Athanasios D. Protogerou, Petros P. Sfikakis, George D. Kitas, Narendra N. Khanna, Zoltan Ruzsa, Aditya M. Sharma, Sanjay Saxena, Gavino Faa, John R. Laird, Amer M. Johri, Manudeep K. Kalra, Kosmas I. Paraskevas and Luca Saba
Diagnostics 2022, 12(3), 722; https://doi.org/10.3390/diagnostics12030722 - 16 Mar 2022
Cited by 23 | Viewed by 3794
Abstract
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the [...] Read more.
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks. Full article
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
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