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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 35164

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Dr. Mauro Castelli

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NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
Interests: machine learning; evolutionary computation; deep learning; pattern recognition; neuro-evolution
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Special Issue Information

Dear Colleagues,

Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex and heterogeneous data. In order to tackle the growing complexity associated with emerging and future life science challenges, bioinformatics and computational biology researchers need to explore, develop, and apply novel computational concepts, methods, tools, and systems.

Recent years have seen the rise of deep learning (DL). Thanks to the advances in terms of hardware, algorithms, and availability of data, DL has been used successfully to address complex problems that would have been impossible to address a few decades ago. Nonetheless, the revolution brought by DL techniques is just in its early infancy, with new contributions and new ideas constantly being proposed and published.

A related approach comes from the field of neuro-evolution, the use of evolutionary algorithms to optimize DL architectures. Neuro-evolution has the potential to achieve better performance with respect to DL-based models, considering that it can optimize the whole architecture, its hyperparameters, and the learning algorithm.

The objective of this Special Issue is to invite active researchers in the field of DL to present original research articles that focus on the development and application of new DL architectures for addressing complex problems in the fields of biomedicine and bioinformatics. Hybrid techniques combining both DL with neuro-evolution are particularly welcome.

Dr. Mauro Castelli
Guest Editor

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Keywords

Deep learning
Medicine
Bioinformatics
Neuro-evolution
Image enhancement
Image segmentation
miRNA–mRNA target prediction
Hybrid DL methods
Classification of medical data
Patient monitoring
Knowledge discovery
Data analysis
Automatic design of DL architectures

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Published Papers (6 papers)

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Editorial

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2 pages, 158 KiB

Open AccessEditorial

Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics

by Mauro Castelli

Appl. Sci. 2022, 12(15), 7924; https://doi.org/10.3390/app12157924 - 8 Aug 2022

Viewed by 1196

Abstract

Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex and heterogeneous data [...] Full article

(This article belongs to the Special Issue Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)

Research

Jump to: Editorial, Other

16 pages, 3436 KiB

Open AccessArticle

Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume

by Shing-Hong Liu, Ren-Xuan Li, Jia-Jung Wang, Wenxi Chen and Chun-Hung Su

Appl. Sci. 2020, 10(13), 4612; https://doi.org/10.3390/app10134612 - 3 Jul 2020

Cited by 35 | Viewed by 7737

Abstract

As photoplethysmographic (PPG) signals are comprised of numerous pieces of important physiological information, they have been widely employed to measure many physiological parameters. However, only a high-quality PPG signal can provide a reliable physiological assessment. Unfortunately, PPG signals are easily corrupted by motion artifacts and baseline drift during recording. Although several rule-based algorithms have been developed for evaluating the quality of PPG signals, few artificial intelligence-based algorithms have been presented. Thus, this study aims to classify the quality of PPG signals by using two two-dimensional deep convolution neural networks (DCNN) when the PPG pulse is used to measure cardiac stroke volume (SV) by impedance cardiography. An image derived from a PPG pulse and its differential pulse is used as the input to the two DCNN models. To quantify the quality of individual PPG pulses, the error percentage of the beat-to-beat SV measured by our device and medis^® CS 2000 synchronously is used to determine whether the pulse quality is high, middle, or low. Fourteen subjects were recruited, and a total of 3135 PPG pulses (1342 high quality, 73 middle quality, and 1720 low quality) were obtained. We used a traditional DCNN, VGG-19, and a residual DCNN, ResNet-50, to determine the quality levels of the PPG pulses. Their results were all better than the previous rule-based methods. The accuracies of VGG-19 and ResNet-50 were 0.895 and 0.925, respectively. Thus, the proposed DCNN may be applied for the classification of PPG quality and be helpful for improving the SV measurement in impedance cardiography. Full article

(This article belongs to the Special Issue Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)

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20 pages, 3281 KiB

Open AccessArticle

How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset

by Ibrahem Kandel and Mauro Castelli

Appl. Sci. 2020, 10(10), 3359; https://doi.org/10.3390/app10103359 - 12 May 2020

Cited by 60 | Viewed by 11920

Abstract

Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers. Full article

(This article belongs to the Special Issue Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)

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19 pages, 1515 KiB

Open AccessArticle

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

by Paulo Lapa, Mauro Castelli, Ivo Gonçalves, Evis Sala and Leonardo Rundo

Appl. Sci. 2020, 10(1), 338; https://doi.org/10.3390/app10010338 - 2 Jan 2020

Cited by 22 | Viewed by 3316

Abstract

Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN. Full article

(This article belongs to the Special Issue Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)

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18 pages, 6006 KiB

Open AccessArticle

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

by Xianghong Lin and Jianyang Zheng

Appl. Sci. 2019, 9(18), 3876; https://doi.org/10.3390/app9183876 - 16 Sep 2019

Cited by 12 | Viewed by 3263

Abstract

Neurons are the basic building and computational units of the nervous system, and have complex and diverse spatial geometric structures. By solving the neuronal classification problem, we can further understand the characteristics of neurons and the process of information transmission. This paper presents a neuronal morphology classification approach based on locally cumulative connected deep neural networks, where 43 geometric features were extracted from two different neuron datasets and applied to classify types of neurons. Then, the effects of different parameters of deep learning networks on the performance of neuron classification were analyzed including mini-batch size, number of intermediate layers, and number of building blocks. The accuracy of the approach was also compared with that of the other mainstream machine learning approaches. The experimental results showed that the proposed approach is effective for solving complex neuronal morphology classification problems. Full article

(This article belongs to the Special Issue Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)

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Other

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35 pages, 4189 KiB

Open AccessHypothesis

Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

by Sushruta Mishra, Pradeep Kumar Mallick, Hrudaya Kumar Tripathy, Akash Kumar Bhoi and Alfonso González-Briones

Appl. Sci. 2020, 10(22), 8137; https://doi.org/10.3390/app10228137 - 17 Nov 2020

Cited by 75 | Viewed by 6625

Abstract

There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively. Full article

(This article belongs to the Special Issue Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics)

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