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Processes
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Machine Learning in Biomaterials, Biostructures and Bioinformatics
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Machine Learning in Biomaterials, Biostructures and Bioinformatics

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 16922

Special Issue Editors

Dr. Jie Sun

E-Mail Website
Guest Editor
Department of Mechatronics and Robotics, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: biofabrication; machine learning; additive manufacturing; scaffold-based cell culture
Prof. Dr. Kaizhu Huang

E-Mail Website
Guest Editor
Data Science Research Center, Duke Kunshan University, Kunshan 215316, China
Interests: machine learning; pattern recognition; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sanjairaj Vijayavenkataraman

E-Mail Website
Guest Editor
1. Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA
2. The Vijay Lab, Division of Engineering, New York University Abu Dhabi, Abu Dhabi 129188, United Arab Emirates
Interests: additive manufacturing; 3D bioprinting; biomaterials; polymers; tissue engineering and regenerative medicine; 3D printed scaffolds for tissue engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in bioprinting have provided a versatile technology platform for the rapid manufacturing of engineered biomaterials and smart structures. At the same time, there has been a surge in the use of machine learning (ML) to bioprinting-relevant research such as medical imaging and segmentation, optimization of bioinks or bioprinting processes, as well as in vitro parametric studies. Applying machine learning to design biomaterials, biofabrication, and biostructures is advantageous in terms of reducing experiment costs, developing customized designs, and optimizing materials and structures and biomechanical performance, yet there is still a significant knowledge gap preventing us from achieving the desired performance and functionality. This Special Issue aims to present a snapshot of the latest developments and current trends in this topic and address various novel applications to advance the current bioprinting technologies.

Dr. Jie Sun
Prof. Dr. Kaizhu Huang
Dr. Sanjairaj Vijayavenkataraman
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. Processes is an international peer-reviewed open access monthly 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

  • machine learning
  • biomaterials
  • biofabrication
  • biostructures
  • bioinformatics

Published Papers (4 papers)

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Research

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27 pages, 7950 KiB  
Article
Multi-Models of Analyzing Dermoscopy Images for Early Detection of Multi-Class Skin Lesions Based on Fused Features
by Ibrahim Abdulrab Ahmed, Ebrahim Mohammed Senan, Hamzeh Salameh Ahmad Shatnawi, Ziad Mohammad Alkhraisha and Mamoun Mohammad Ali Al-Azzam
Processes 2023, 11(3), 910; https://doi.org/10.3390/pr11030910 - 16 Mar 2023
Cited by 5 | Viewed by 2454
Abstract
Melanoma is a cancer that threatens life and leads to death. Effective detection of skin lesion types by images is a challenging task. Dermoscopy is an effective technique for detecting skin lesions. Early diagnosis of skin cancer is essential for proper treatment. Skin [...] Read more.
Melanoma is a cancer that threatens life and leads to death. Effective detection of skin lesion types by images is a challenging task. Dermoscopy is an effective technique for detecting skin lesions. Early diagnosis of skin cancer is essential for proper treatment. Skin lesions are similar in their early stages, so manual diagnosis is difficult. Thus, artificial intelligence techniques can analyze images of skin lesions and discover hidden features not seen by the naked eye. This study developed hybrid techniques based on hybrid features to effectively analyse dermoscopic images to classify two datasets, HAM10000 and PH2, of skin lesions. The images have been optimized for all techniques, and the problem of imbalance between the two datasets has been resolved. The HAM10000 and PH2 datasets were classified by pre-trained MobileNet and ResNet101 models. For effective detection of the early stages skin lesions, hybrid techniques SVM-MobileNet, SVM-ResNet101 and SVM-MobileNet-ResNet101 were applied, which showed better performance than pre-trained CNN models due to the effectiveness of the handcrafted features that extract the features of color, texture and shape. Then, handcrafted features were combined with the features of the MobileNet and ResNet101 models to form a high accuracy feature. Finally, features of MobileNet-handcrafted and ResNet101-handcrafted were sent to ANN for classification with high accuracy. For the HAM10000 dataset, the ANN with MobileNet and handcrafted features achieved an AUC of 97.53%, accuracy of 98.4%, sensitivity of 94.46%, precision of 93.44% and specificity of 99.43%. Using the same technique, the PH2 data set achieved 100% for all metrics. Full article
(This article belongs to the Special Issue Machine Learning in Biomaterials, Biostructures and Bioinformatics)
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27 pages, 9503 KiB  
Article
Hybrid Techniques of Analyzing MRI Images for Early Diagnosis of Brain Tumours Based on Hybrid Features
by Badiea Abdulkarem Mohammed, Ebrahim Mohammed Senan, Talal Sarheed Alshammari, Abdulrahman Alreshidi, Abdulaziz M. Alayba, Meshari Alazmi and Afrah N. Alsagri
Processes 2023, 11(1), 212; https://doi.org/10.3390/pr11010212 - 9 Jan 2023
Cited by 8 | Viewed by 2242
Abstract
Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images [...] Read more.
Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images provide details of the brain’s internal structure, which allow radiologists and doctors to diagnose brain tumours. However, MR images contain complex details that require highly qualified experts and a long time to analyse. Artificial intelligence techniques solve these challenges. This paper presents four proposed systems, each with more than one technology. These techniques vary between machine, deep and hybrid learning. The first system comprises artificial neural network (ANN) and feedforward neural network (FFNN) algorithms based on the hybrid features between local binary pattern (LBP), grey-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. The second system comprises pre-trained GoogLeNet and ResNet-50 models for dataset classification. The two models achieved superior results in distinguishing between the types of brain tumours. The third system is a hybrid technique between convolutional neural network and support vector machine. This system also achieved superior results in distinguishing brain tumours. The fourth proposed system is a hybrid of the features of GoogLeNet and ResNet-50 with the LBP, GLCM and DWT algorithms (handcrafted features) to obtain representative features and classify them using the ANN and FFNN. This method achieved superior results in distinguishing between brain tumours and performed better than the other methods. With the hybrid features of GoogLeNet and hand-crafted features, FFNN achieved an accuracy of 99.9%, a precision of 99.84%, a sensitivity of 99.95%, a specificity of 99.85% and an AUC of 99.9%. Full article
(This article belongs to the Special Issue Machine Learning in Biomaterials, Biostructures and Bioinformatics)
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18 pages, 5157 KiB  
Article
Information Visualisation for Antibiotic Detection Biochip Design and Testing
by Paul Craig, Ruben Ng, Boris Tefsen, Sam Linsen, Yu Liu and Joshua Hendel
Processes 2022, 10(12), 2680; https://doi.org/10.3390/pr10122680 - 13 Dec 2022
Cited by 1 | Viewed by 1402
Abstract
Biochips are engineered substrates that have different spots that change colour according to biochemical reactions. These spots can be read together to detect different analytes (such as different types of antibiotic, pathogens, or biological agents). While some chips are designed so that each [...] Read more.
Biochips are engineered substrates that have different spots that change colour according to biochemical reactions. These spots can be read together to detect different analytes (such as different types of antibiotic, pathogens, or biological agents). While some chips are designed so that each spot on its own can detect a particular analyte, chip designs that use a combination of spots to detect different analytes can be more efficient and detect a larger number of analytes with a smaller number of spots. These types of chip can, however, be more difficult to design, as an efficient and effective combination of biosensors needs to be selected for the chip. These need to be able to differentiate between a range of different analytes so the values can be combined in a way that demonstrates the confidence that a particular analyte is present or not. The study described in this paper examines the potential for information visualisation to support the process of designing and reading biochips by developing and evaluating applications that allow biologists to analyse the results of experiments aimed at detecting candidate bio-sensors (to be used as biochip spots) and examining how biosensors can combine to identify different analytes. Our results demonstrate the potential of information visualisation and machine learning techniques to improve the design of biochips. Full article
(This article belongs to the Special Issue Machine Learning in Biomaterials, Biostructures and Bioinformatics)
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Review

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16 pages, 1141 KiB  
Review
Machine Learning Methods in Skin Disease Recognition: A Systematic Review
by Jie Sun, Kai Yao, Guangyao Huang, Chengrui Zhang, Mark Leach, Kaizhu Huang and Xi Yang
Processes 2023, 11(4), 1003; https://doi.org/10.3390/pr11041003 - 26 Mar 2023
Cited by 1 | Viewed by 10038
Abstract
Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and [...] Read more.
Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to collate and analyze machine learning (ML) applications in skin lesion research, with the goal of encouraging the development of automated systems for skin disease diagnosis. To assist dermatologists in their clinical diagnosis, several skin image datasets have been developed and published online. Such efforts have motivated researchers and medical staff to develop automatic skin diagnosis systems using image segmentation and classification processes. This paper summarizes the fundamental steps in skin lesion diagnosis based on papers mainly published since 2013. The applications of ML methods (including traditional ML and deep learning (DL)) in skin disease recognition are reviewed based on their contributions, methods, and achieved results. Such technical analysis is beneficial to the continuing development of reliable and effective computer-aided skin disease diagnosis systems. We believe that more research efforts will lead to the current automatic skin diagnosis studies being used in real clinical settings in the near future. Full article
(This article belongs to the Special Issue Machine Learning in Biomaterials, Biostructures and Bioinformatics)
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