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Emerging Trends of Deep Learning in Medical Imaging: Challenges and Methodologies

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Special Issue Editors

Dr. Fahmi Khalifa

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Guest Editor

Assistant Professor, Mansoura University, Egypt and Postdoctoral associate, University of Louisville, USA
Interests: medical signal/image processing; machine learning; deep learning; pattern recognition; image segmentation; image/shape registration; image encryption
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmed Shalaby

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Guest Editor

University of Louisville, USA
Interests: computer vision; image processing; medical imaging; bioengineering
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmed Soliman

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Guest Editor

Research Scientist, University of Louisville, Louisville, KY, USA
Interests: signal processing; probability; random variables; stochastic processes and pattern recognition; image segmentation and image/shape registration; stereo reconstruction; image formation; face detection; face recognition; object tracking and shape-from-shading

Special Issue Information

Dear Colleagues,

The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of medical image analysis to examine the structure and assess the function of human organs and/or to provide early prediction and assessment of diseases. Conventional machine learning methods have been the focus of intense investigation for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs to deal with complexities of medical data by radically changing research methodologies toward a data-oriented approach. This Special Issue calls for recent studies and research work focusing on deep learning applications for medical image analysis. Papers of both theoretical and applicative nature are welcome, as well as high-quality review and survey papers for the medical image analysis research community. Major topics of interest include but are not restricted to the following:

Artificial intelligence paradigms for medical image, processing, analysis, and diagnosis;
Novel deep learning architectures for efficient feature extraction and classification;
Optimization techniques for DL architectures that focus on medical image processing/analysis;
Unsupervised and/or semi-supervised DL approaches to deal with small or poorly annotated medical data sets;
Augmented and hybrid learning tools, e.g., generative adversarial network (GAN), Bayesian GANs, Neural style transfer, etc.;
Multi-level fusion techniques that can maximize the benefits of integrating multiple/complex data sources with different imaging modalities;
Deep learning application for big medical data;
Transfer learning techniques for medical data.

Dr. Fahmi Khalifa
Dr. Ahmed Shalaby
Dr. Ahmed Soliman
Guest Editors

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Keywords

deep learning
artificial intelligence
big data
medical imaging analysis
optimization
data augmentation

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18 pages, 1857 KiB

Open AccessArticle

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

by Abeer A. AbdElhamid, Eman AbdElhalim, Mohamed A. Mohamed and Fahmi Khalifa

Appl. Sci. 2022, 12(4), 2080; https://doi.org/10.3390/app12042080 - 17 Feb 2022

Cited by 12 | Viewed by 2961

Abstract

Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a

10^{- 4}

learning rate. Full article

(This article belongs to the Special Issue Emerging Trends of Deep Learning in Medical Imaging: Challenges and Methodologies)

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16 pages, 1236 KiB

Open AccessArticle

Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

by Falko Lavitt, Demi J. Rijlaarsdam, Dennet van der Linden, Ewelina Weglarz-Tomczak and Jakub M. Tomczak

Appl. Sci. 2021, 11(11), 4912; https://doi.org/10.3390/app11114912 - 27 May 2021

Cited by 20 | Viewed by 8052

Abstract

In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features). Full article

(This article belongs to the Special Issue Emerging Trends of Deep Learning in Medical Imaging: Challenges and Methodologies)

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