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Deep and Machine Learning for Image Processing: Medical and Non-medical Applications

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 47282

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Special Issue Editors

Dr. Mohamed Shehata

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Guest Editor

Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: computer vision; artificial intelligence; machine and deep learning; big data; medical imaging; computer-aided diagnostics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mostafa Elhosseini

E-Mail Website
Guest Editor

Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Interests: Artificial intelligence (AI); machine learning; deep learning; robotics;metaheuristics; computer-assisted diagnosis systems; computer vision; bioinspired optimization algorithms; smart systems engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The new era and recent advances in machine and deep learning approaches have been widely used as indispensable tools in modern artificial intelligence-based image processing systems to perform medical or non-medical tasks. Such systems have proven themselves to be reproducible and have the generalization ability to solve unseen problems and adapt to new conditions. Examples of non-medical image processing applications that utilize machine and deep learning approaches could be image classification, image localization, handwriting recognition, object detection, object tracking, etc. Furthermore, for medical applications, diagnostic and prediction systems for different diseases, image segmentation, and image registration are some areas of potential interest for many researchers in the field of medical image processing using different machine and/or deep learning approaches. This special issue will focus on the state-of-the-art machine and deep learning techniques in image processing for medical and non-medical applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Image processing;
Image localization;
Object detection;
Object tracking;
Medical imaging;
Image segmentation using deep learning;
Applying deep learning for image registration;
AI-based diagnostic systems;
AI-based predictive systems;
AI-based healthcare.

We look forward to receiving your contributions.

Dr. Mohamed Shehata
Prof. Dr. Mostafa Elhosseini
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. Electronics 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

deep learning
machine learning
image processing
pattern recognition
biomedical imaging
diagnostic systems
prediction systems
healthcare systems

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Published Papers (13 papers)

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Editorial

Jump to: Research

7 pages, 184 KiB

Open AccessEditorial

Charting New Frontiers: Insights and Future Directions in ML and DL for Image Processing

by Mohamed Shehata and Mostafa Elhosseini

Electronics 2024, 13(7), 1345; https://doi.org/10.3390/electronics13071345 - 3 Apr 2024

Viewed by 1347

Abstract

The Special Issue “Deep and Machine Learning for Image Processing: Medical and Non-medical Applications” of the MDPI journal Electronics marks a pivotal point in the exploration of machine learning (ML) and deep learning (DL) applications in image processing [...] Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

Research

Jump to: Editorial

15 pages, 2073 KiB

Open AccessArticle

Perceptual Image Quality Prediction: Are Contrastive Language–Image Pretraining (CLIP) Visual Features Effective?

by Chibuike Onuoha, Jean Flaherty and Truong Cong Thang

Electronics 2024, 13(4), 803; https://doi.org/10.3390/electronics13040803 - 19 Feb 2024

Cited by 1 | Viewed by 1854

Abstract

In recent studies, the Contrastive Language–Image Pretraining (CLIP) model has showcased remarkable versatility in downstream tasks, ranging from image captioning and question-answering reasoning to image–text similarity rating, etc. In this paper, we investigate the effectiveness of CLIP visual features in predicting perceptual image quality. CLIP is also compared with competitive large multimodal models (LMMs) for this task. In contrast to previous studies, the results show that CLIP and other LMMs do not always provide the best performance. Interestingly, our evaluation experiment reveals that combining visual features from CLIP or other LMMs with some simple distortion features can significantly enhance their performance. In some cases, the improvements are even more than 10%, while the prediction accuracy surpasses 90%. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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17 pages, 5539 KiB

Open AccessArticle

Learing Sampling and Reconstruction Using Bregman Iteration for CS-MRI

by Tiancheng Fei and Xiangchu Feng

Electronics 2023, 12(22), 4657; https://doi.org/10.3390/electronics12224657 - 15 Nov 2023

Cited by 1 | Viewed by 1087

Abstract

The purpose of compressed sensing magnetic resonance imaging (CS-MRI) is to reconstruct clear images using data from the Nyquist sampling space. By reducing the amount of sampling, MR imaging can be accelerated, thereby improving the efficiency of device data collection and increasing patient throughput. The two basic challenges in CS-MRI are designing sparse sampling masks and designing effective reconstruction algorithms. In order to be consistent with the analysis conclusion of CS theory, we propose a bi-level optimization model to optimize the sampling mask and the reconstruction network at the same time under the constraints of data terms. The proposed sampling sub-network is based on an additive gradient strategy. In our reconstructed subnet, we design a phase deep unfolding network based on the Bregman iterative algorithm to find the solution of constrained problems by solving a series of unconstrained problems. Experiments on two widely used MRI datasets show that our proposed model yields sub-sampling patterns and reconstruction models customized for training data, achieving state-of-the-art results in terms of quantitative metrics and visual quality. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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24 pages, 4157 KiB

Open AccessArticle

Unified Scaling-Based Pure-Integer Quantization for Low-Power Accelerator of Complex CNNs

by Ali A. Al-Hamid and HyungWon Kim

Electronics 2023, 12(12), 2660; https://doi.org/10.3390/electronics12122660 - 13 Jun 2023

Viewed by 1914

Abstract

Although optimizing deep neural networks is becoming crucial for deploying the networks on edge AI devices, it faces increasing challenges due to scarce hardware resources in modern IoT and mobile devices. This study proposes a quantization method that can quantize all internal computations and parameters in the memory modification. Unlike most previous methods that primarily focused on relatively simple CNN models for image classification, the proposed method, Unified Scaling-Based Pure-Integer Quantization (USPIQ), can handle more complex CNN models for object detection. USPIQ aims to provide a systematic approach to convert all floating-point operations to pure-integer operations in every model layer. It can significantly reduce the computational overhead and make it more suitable for low-power neural network accelerator hardware consisting of pure-integer datapaths and small memory aimed at low-power consumption and small chip size. The proposed method optimally calibrates the scale parameters for each layer using a subset of unlabeled representative images. Furthermore, we introduce a notion of the Unified Scale Factor (USF), which combines the conventional two-step scaling processes (quantization and dequantization) into a single process for each layer. As a result, it improves the inference speed and the accuracy of the resulting quantized model. Our experiment on YOLOv5 models demonstrates that USPIQ can significantly reduce the on-chip memory for parameters and activation data by ~75% and 43.68%, respectively, compared with the floating-point model. These reductions have been achieved with a minimal loss in [email protected]—at most 0.61%. In addition, our proposed USPIQ exhibits a significant improvement in the inference speed compared to ONNX Run-Time quantization, achieving a speedup of 1.64 to 2.84 times. We also demonstrate that USPIQ outperforms the previous methods in terms of accuracy and hardware reduction for 8-bit quantization of all YOLOv5 versions. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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16 pages, 2467 KiB

Open AccessArticle

Camouflaged Object Detection with a Feature Lateral Connection Network

by Tao Wang, Jian Wang and Ruihao Wang

Electronics 2023, 12(12), 2570; https://doi.org/10.3390/electronics12122570 - 7 Jun 2023

Cited by 4 | Viewed by 2201

Abstract

We propose a new framework for camouflaged object detection (COD) named FLCNet, which comprises three modules: an underlying feature mining module (UFM), a texture-enhanced module (TEM), and a neighborhood feature fusion module (NFFM). Existing models overlook the analysis of underlying features, which results in extracted low-level feature texture information that is not prominent enough and contains more interference due to the slight difference between the foreground and background of the camouflaged object. To address this issue, we created a UFM using convolution with various expansion rates, max-pooling, and avg-pooling to deeply mine the textural information of underlying features and eliminate interference. Motivated by the traits passed down through biological evolution, we created an NFFM, which primarily consists of element multiplication and concatenation followed by an addition operation. To obtain precise prediction maps, our model employs the top-down strategy to gradually combine high-level and low-level information. Using four benchmark COD datasets, our proposed framework outperforms 21 deep-learning-based models in terms of seven frequently used indices, demonstrating the effectiveness of our methodology. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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31 pages, 8630 KiB

Open AccessArticle

Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module

by Weihao Chen, Lumei Su, Zhiwei Lin, Xinqiang Chen and Tianyou Li

Electronics 2023, 12(9), 2126; https://doi.org/10.3390/electronics12092126 - 6 May 2023

Viewed by 2085

Abstract

Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult. Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads to the poor performance of traditional detection methods. This paper proposes an end-to-end instance segmentation method using the multi-instance relation weighting module for irregular deformable objects. To solve the problem of introducing redundant background information when using the horizontal rectangular box detector, the Mask Scoring R-CNN is used to perform pixel-level instance segmentation so that the bounding box can accurately surround the irregular objects. Considering that deformable objects in power operation workplaces often appear with construction personnel and the objects have an apparent correlation, a multi-instance relation weighting module is proposed to fuse the appearance features and geometric features of objects so that the relation features between objects are learned end-to-end to improve the segmentation effect of irregular objects. The segmentation mAP on the self-built dataset of irregular deformable objects for electric power operation workplaces reached up to 44.8%. With the same 100,000 training rounds, the bounding box mAP and segmentation mAP improved by 1.2% and 0.2%, respectively, compared with the MS R-CNN. Finally, in order to further verify the generalization performance and practicability of the proposed method, an intelligent monitoring system for the power operation scenes is designed to realize the actual deployment and application of the proposed method. Various tests show that the proposed method can segment irregular deformable objects well. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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14 pages, 4746 KiB

Open AccessArticle

Long-Distance Person Detection Based on YOLOv7

by Fan Tang, Fang Yang and Xianqing Tian

Electronics 2023, 12(6), 1502; https://doi.org/10.3390/electronics12061502 - 22 Mar 2023

Cited by 21 | Viewed by 7478

Abstract

In the research field of small object detection, most object detectors have been successfully used for pedestrian detection, face recognition, lost and found, and automatic driving, among other applications, and have achieved good results. However, when general object detectors encounter challenging low-resolution images from the TinyPerson dataset, they will produce undesirable detection results because of the dense occlusion between people and different body poses. In order to solve these problems, this paper proposes a tiny object detection method TOD-YOLOv7 based on YOLOv7.First, this paper presents a reconstruction of the YOLOv7 network by adding a tiny object detection layer to enhance its detection ability. Then, we use the recursive gated convolution module to realize the interaction with the higher-order space to accelerate the model initialization process and reduce the reasoning time. Secondly, this paper proposes the integration of a coordinate attention mechanism into the YOLOv7 feature extraction network to strengthen the pedestrian object information and weaken the background information.Additionally, we leverage data augmentation techniques to improve the representation learning of the algorithm. The results show that compared with the baseline model YOLOv7, the detection accuracy of this model on the TinyPerson dataset is improved from 7.1% to 9.5%, and the detection speed reaches 208 frames per second (FPS). The algorithm of this paper is shown to achieve better detection results for tiny object detection. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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16 pages, 563 KiB

Open AccessArticle

ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media

by Malik Almaliki, Abdulqader M. Almars, Ibrahim Gad and El-Sayed Atlam

Electronics 2023, 12(4), 1048; https://doi.org/10.3390/electronics12041048 - 20 Feb 2023

Cited by 23 | Viewed by 4530

Abstract

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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16 pages, 8505 KiB

Open AccessArticle

Single Energy X-ray Image Colorization Using Convolutional Neural Network for Material Discrimination

by Bilel Yagoub, Hatem Ibrahem, Ahmed Salem and Hyun-Soo Kang

Electronics 2022, 11(24), 4101; https://doi.org/10.3390/electronics11244101 - 9 Dec 2022

Cited by 3 | Viewed by 3636

Abstract

Colorization in X-ray material discrimination is considered one of the main phases in X-ray baggage inspection systems for detecting contraband and hazardous materials by displaying different materials with specific colors. The substructure of material discrimination identifies materials based on their atomic number. However, the images are checked and assigned by a human factor, which may decelerate the verification process. Therefore, researchers used computer vision and machine learning methods to expedite the examination process and ascertain the precise identification of materials and elements. This study proposes a color-based material discrimination method for single-energy X-ray images based on the dual-energy colorization. We use a convolutional neural network to discriminate materials into several classes, such as organic, non-organic substances, and metals. It highlights the details of the objects, including occluded objects, compared to commonly used segmentation methods, which do not show the details of the objects. We trained and tested our model on three popular X-ray datasets, which are Korean datasets comprising three kinds of scanners: (Rapiscan, Smith, Astrophysics), SIXray, and COMPASS-XP. The results showed that the proposed method achieved high performance in X-ray colorization in terms of peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS). We applied the trained models to the single-energy X-ray images and we compared the results obtained from each model. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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19 pages, 1213 KiB

Open AccessArticle

DL-MDF-OH²: Optimized Deep Learning-Based Monkeypox Diagnostic Framework Using the Metaheuristic Harris Hawks Optimizer Algorithm

by Saleh Ateeq Almutairi

Electronics 2022, 11(24), 4077; https://doi.org/10.3390/electronics11244077 - 8 Dec 2022

Cited by 19 | Viewed by 2459

Abstract

At the time the world is attempting to get over the damage caused by the COVID-19 spread, the monkeypox virus threatens to evolve into a global pandemic. Human monkeypox was first recognized in Africa and has recently emerged in 103 countries outside Africa. However, monkeypox diagnosis in an early stage is difficult because of the similarity between it, chickenpox, cowpox and measles. In some cases, computer-assisted detection of monkeypox lesions can be helpful for quick identification of suspected cases. Infected and uninfected cases have added to a growing dataset that is publicly accessible and may be utilized by machine and deep learning to predict the suspected cases at an early stage. Motivated by this, a diagnostic framework to categorize the cases of patients into four categories (i.e., normal, monkeypox, chicken pox and measles) is proposed. The diagnostic framework is a hybridization of pre-trained Convolution Neural Network (CNN) models, machine learning classifiers and a metaheuristic optimization algorithm. The hyperparameters of the five pre-trained models (i.e., VGG19, VGG16, Xception, MobileNet and MobileNetV2) are optimized using a Harris Hawks Optimizer (HHO) metaheuristic algorithm. After that, the features can be extracted from the feature extraction and reduction layers. These features are classified using seven machine learning models (i.e., Random Forest, AdaBoost, Histogram Gradient Boosting, Gradient Boosting, Support Vector Machine, Extra Trees and KNN). For each classifier, 10-fold cross-validation is used to train and test the classifiers on the features and the weighted average performance metrics are reported. The predictions from the pre-trained model and machine learning classifiers are then processed using majority voting. This study conducted the experiments on two datasets (i.e., Monkeypox Skin Images Dataset (MSID) and Monkeypox Images Dataset (MPID)). MSID dataset values 97.67%, 95.19%, 97.96%, 95.11%, 96.58%, 95.10%, 90.93% and 96.65% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. While for the MPID dataset, values of 97.51%, 94.84%, 94.48%, 94.96%, 96.66%, 94.88%, 90.45% and 96.69% are achieved concerning accuracy, sensitivity, specificity, PPV, BAC, F1, IoU and ROC, respectively. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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16 pages, 1827 KiB

Open AccessArticle

Computer-Aided Diagnosis for Early Signs of Skin Diseases Using Multi Types Feature Fusion Based on a Hybrid Deep Learning Model

by Saleh Naif Almuayqil, Sameh Abd El-Ghany and Mohammed Elmogy

Electronics 2022, 11(23), 4009; https://doi.org/10.3390/electronics11234009 - 2 Dec 2022

Cited by 8 | Viewed by 9182

Abstract

According to medical reports and statistics, skin diseases have millions of victims worldwide. These diseases might affect the health and life of patients and increase the costs of healthcare services. Delays in diagnosing such diseases make it difficult to overcome the consequences of these types of disease. Usually, diagnosis is performed using dermoscopic images, where specialists utilize certain measures to produce the results. This approach to diagnosis faces multiple disadvantages, such as overlapping infectious and inflammatory skin diseases and high levels of visual diversity, obstructing accurate diagnosis. Therefore, this article uses medical image analysis and artificial intelligence to present an automatic diagnosis system of different skin lesion categories using dermoscopic images. The addressed diseases are actinic keratoses (solar keratoses), benign keratosis (BKL), melanocytic nevi (NV), basal cell carcinoma (BCC), dermatofibroma (DF), melanoma (MEL), and vascular skin lesions (VASC). The proposed system consists of four main steps: (i) preprocessing the input raw image data and metadata; (ii) feature extraction using six pre-trained deep learning models (i.e., VGG19, InceptionV3, ResNet50, DenseNet201, and Xception); (iii) features concatenation; and (iv) classification/diagnosis using machine learning techniques. The evaluation results showed an average accuracy, sensitivity, specificity, precision, and disc similarity coefficient (DSC) of around 99.94%, 91.48%, 98.82%, 97.01%, and 94.00%, respectively. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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14 pages, 2677 KiB

Open AccessArticle

SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia

by Talal H. Noor, Abdulqader M. Almars, Majed Alwateer, Malik Almaliki, Ibrahim Gad and El-Sayed Atlam

Electronics 2022, 11(23), 3986; https://doi.org/10.3390/electronics11233986 - 1 Dec 2022

Cited by 10 | Viewed by 5146

Abstract

Crimes have clearly had a detrimental impact on a nation’s development, prosperity, reputation, and economy. The issue of crime has become one of the most pressing concerns in societies, thus reducing the crime rate has become an increasingly critical task. Recently, several studies have been proposed to identify the causes and occurrences of crime in order to identify ways to reduce crime rates. However, few studies have been conducted in Saudi Arabia technological solutions based on crime analysis. The analysis of crime can help governments identify hotspots of crime and monitor crime distribution. This study aims to investigate which Saudi Arabian areas will experience increased crime rates in the coming years. This research helps law enforcement agencies to effectively utilize available resources in order to reduce crime rates. This paper proposes SARIMA model which focuses on identifying factors that affect crimes in Saudi Arabia, estimating a reasonable crime rate, and identifying the likelihood of crime distribution based on various locations. The dataset used in this study is obtained from Saudi Arabian official government channels. There is detailed information related to time and place along with crime statistics pertaining to different types of crimes. Furthermore, the new proposed method performs better than other traditional classifiers such as Linear Regression, XGB, and Random Forest. Finally, SARIMA model has an MAE score of 0.066559, which is higher than the other models. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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19 pages, 440 KiB

Open AccessArticle

Diagnosis Myocardial Infarction Based on Stacking Ensemble of Convolutional Neural Network

by Hela Elmannai, Hager Saleh, Abeer D. Algarni, Ibrahim Mashal, Kyung Sup Kwak, Shaker El-Sappagh and Sherif Mostafa

Electronics 2022, 11(23), 3976; https://doi.org/10.3390/electronics11233976 - 30 Nov 2022

Cited by 5 | Viewed by 1716

Abstract

Artificial Intelligence (AI) technologies are vital in identifying patients at risk of serious illness by providing an early hazards risk. Myocardial infarction (MI) is a silent disease that has been harvested and is still threatening many lives. The aim of this work is to propose a stacking ensemble based on Convolutional Neural Network model (CNN). The proposed model consists of two primary levels, Level-1 and Level-2. In Level-1, the pre-trained CNN models (i.e., CNN-Model1, CNN-Model2, and CNN-Model3) produce the output probabilities and collect them in stacking for the training and testing sets. In Level-2, four meta-leaner classifiers (i.e., SVM, LR, RF, or KNN) are trained by stacking the output probabilities of the training set and are evaluated using the stacking of the output probabilities of the testing set to make the final prediction results. The proposed work was evaluated based on two ECG heartbeat signals datasets for MI: Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) and Physikalisch-Technische Bundesanstalt (PTB) datasets. The proposed model was compared with a diverse set of classical machine learning algorithms such as decision tree, K-nearest neighbor, and support vector machine, and the three base CNN classifiers of CNN-Model1, CNN-Model2, and CNN-Model3. The proposed model based on the RF meta-learner classifier obtained the highest scores, achieving remarkable results on both databases used. For the MIT-BIH dataset it achieved an accuracy of 99.8%, precision of 97%, recall of 96%, and F1-score of 94.4%, outperforming all other methods. while with PTB dataset achieved an accuracy of 99.7%, precision of 99%, recall of 99%, and F1-score of 99%, exceeding the other methods. Full article

(This article belongs to the Special Issue Deep and Machine Learning for Image Processing: Medical and Non-medical Applications)

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