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Applied Sciences
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Pattern Recognition in Biomedical Informatics
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Pattern Recognition in Biomedical Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 8228

Special Issue Editor

Dr. Jorge Igual

E-Mail Website
Guest Editor
Instituto de Telecomunicaciones y Aplicaciones Multimedia (ITEAM), Departamento de Comunicaciones, Universitat Politècnica de València, València, Spain
Interests: classification; pattern recognition; statistical signal processing; photography; independent component analysis; machine learning; non negative matrix factorization; biomedical signal processing; neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One research area where new machine-learning techniques are making a huge impact is biomedicine. This is because of the general reasons behind the success of any deep-learning approach, e.g., the huge amounts of data and computational power, but also thanks to some particular characteristics in the field. For example, in some biomedical applications, not only is the supervision very expensive, but even the experts are not able to recognize some patterns in the original data. The ability of the machine-learning methods to detect and extract these informative features to solve a clinical problem (detection, classification, segmentation, etc.) is one key element to explain their success.

In this Special Issue, we are looking for contributions in any biomedical area in this direction. We are interested in how machine-learning algorithms, from traditional pattern recognition approaches to modern deep-learning ones, can be useful in any biomedical problem. In being useful, we do not just mean the typical approaches to label some biomedical data, e.g., to classify a recording as healthy or not, but also other, more innovative contributions such as to generate synthetic data. In the same way, the Special Issue is open to traditional signals such as ECG, EEG, MRI, DNA, and so on, as well as new healthcare-system applications.

Dr. Jorge Igual
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

  • biomedicine
  • machine learning
  • pattern recognition
  • deep learning
  • informatics
  • healthcare
  • clinical imaging
  • artificial intelligence
  • biomedical signals

Published Papers (4 papers)

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Research

17 pages, 3169 KiB  
Article
Lung Cancer Detection Model Using Deep Learning Technique
by Abdul Rahaman Wahab Sait
Appl. Sci. 2023, 13(22), 12510; https://doi.org/10.3390/app132212510 - 20 Nov 2023
Cited by 1 | Viewed by 1820
Abstract
Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate. Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and diagnosis. It can identify early signs of LC using positron emission tomography (PET) and computed [...] Read more.
Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate. Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and diagnosis. It can identify early signs of LC using positron emission tomography (PET) and computed tomography (CT) images. However, the existing DL-based LC detection models demand substantial computational resources. Healthcare centers face challenges in handling the complexities in the model implementation. Therefore, the author aimed to build a DL-based LC detection model using PET/CT images. Effective image preprocessing and augmentation techniques were followed to overcome the noises and artifacts. A convolutional neural network (CNN) model was constructed using the DenseNet-121 model for feature extraction. The author applied deep autoencoders to minimize the feature dimensionality. The MobileNet V3-Small model was used to identify the types of LC using the features. The author applied quantization-aware training and early stopping strategies to improve the proposed LC detection accuracy with less computational power. In addition, the Adam optimization (AO) algorithm was used to fine-tune the hyper-parameters in order to reduce the training time for detecting the LC type. The Lung-PET-CT-Dx dataset was used for performance evaluation. The experimental outcome highlighted that the proposed model obtained an accuracy of 98.6 and a Cohen’s Kappa value of 95.8 with fewer parameters. The proposed model can be implemented in real-time to support radiologists and physicians in detecting LC in the earlier stages. In the future, liquid neural networks and ensemble learning techniques will be used to enhance the performance of the proposed LC detection model. Full article
(This article belongs to the Special Issue Pattern Recognition in Biomedical Informatics)
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20 pages, 5100 KiB  
Article
Skin Lesion Classification Using Hybrid Convolutional Neural Network with Edge, Color, and Texture Information
by Changmin Kim, Myeongsoo Jang, Younghwan Han, Yousik Hong and Woobeom Lee
Appl. Sci. 2023, 13(9), 5497; https://doi.org/10.3390/app13095497 - 28 Apr 2023
Cited by 3 | Viewed by 2131
Abstract
Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 [...] Read more.
Herein, a new paradigm based on deep learning was proposed that allows the extraction of fine-grained differences between skin lesions in pixel units for high accuracy classification of skin lesions. As basic feature information for a dermoscopic image of a skin region, 50 different features were extracted based on the edge, color, and texture features of the skin lesion image. For the edge features, a line-segment-type analysis algorithm was used, wherein the visual information of a dermoscopic image was precisely analyzed in terms of the units of pixels and was transformed into a structured pattern. Regarding the color features of skin lesions, the dermoscopic image was transformed into multiple color models, and the features were acquired by analyzing histograms showing information regarding the distribution of pixel intensities. Subsequently, texture features were extracted by applying the well-known Law’s texture energy measure algorithm. Feature data (50 × 256) generated via the feature extraction process above were used to classify skin lesions via a one-dimensional (1D) convolution layer-based classification model. Because the architecture of the designed model comprises parallel 1D convolution layers, fine-grained features of the dermoscopic image can be identified using different parameters. To evaluate the performance of the proposed method, datasets from the 2017 and 2018 International Skin Imaging Collaboration were used. A comparison of results yielded by well-known classification models and other models reported in the literature show the superiority of the proposed model. Additionally, the proposed method achieves an accuracy exceeding 88%. Full article
(This article belongs to the Special Issue Pattern Recognition in Biomedical Informatics)
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15 pages, 1985 KiB  
Article
Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA
by Christos Koroulis, Evangelos Makris, Angelos Kolaitis, Panayiotis Tsanakas and Christos Pavlatos
Appl. Sci. 2023, 13(8), 5168; https://doi.org/10.3390/app13085168 - 21 Apr 2023
Viewed by 1063
Abstract
The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more [...] Read more.
The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction. Full article
(This article belongs to the Special Issue Pattern Recognition in Biomedical Informatics)
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14 pages, 3680 KiB  
Article
Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
by Laëtitia Launet, Yuandou Wang, Adrián Colomer, Jorge Igual, Cristian Pulgarín-Ospina, Spiros Koulouzis, Riccardo Bianchi, Andrés Mosquera-Zamudio, Carlos Monteagudo, Valery Naranjo and Zhiming Zhao
Appl. Sci. 2023, 13(2), 919; https://doi.org/10.3390/app13020919 - 09 Jan 2023
Cited by 3 | Viewed by 2487
Abstract
Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative [...] Read more.
Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes. Full article
(This article belongs to the Special Issue Pattern Recognition in Biomedical Informatics)
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