Search

Medical imaging, specifically chest X-ray image analysis, is a crucial component of early disease detection and screening in healthcare. Deep learning techniques, such as convolutional neural networks (CNNs), have emerged as powerful tools for computer-aided diagnosis (CAD) in chest X-ray image analysis. These techniques have shown promising results in automating tasks such as classification, detection, and segmentation of abnormalities in chest X-ray images, with the potential to surpass human radiologists. In this review, we provide an overview of the importance of chest X-ray image analysis, historical developments, impact of deep learning techniques, and availability of labeled databases. We specifically focus on advancements and challenges in radiology report generation using deep learning, highlighting potential future advancements in this area. The use of deep learning for report generation has the potential to reduce the burden on radiologists, improve patient care, and enhance the accuracy and efficiency of chest X-ray image analysis in medical imaging., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mohammed Yasser Ouis reports financial support was provided by New Brunswick Innovation Foundation. Mohammed Yasser Ouis reports financial support was provided by New Brunswick Health Research Foundation., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images-437 benign, 210, malignant, and 133 normal-were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1-84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided detection of COVID-19 in CXR imaging using deep and conventional radiomic features. First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE) with internal dense layers to extract low-dimensional deep radiomics. We used Johnson-Lindenstrauss (JL) lemma, Laplacian scoring (LS), and principal component analysis (PCA) to reduce dimensionality in conventional radiomics. The generated low-dimensional deep and conventional radiomics were integrated to classify COVID-19 from pneumonia and healthy patients. We used 704 CXR images for training the entire model (i.e., U-Net, ConvAE, and feature selection in conventional radiomics). Afterward, we independently validated the whole system using a study cohort of 1597 cases. We trained and tested a random forest model for detecting COVID-19 cases through multivariate binary-class and multiclass classification. The maximal (full multivariate) model using a combination of the two radiomic groups yields performance in classification cross-validated accuracy of 72.6% (69.4-74.4%) for multiclass and 89.6% (88.4-90.7%) for binary-class classification.

This work presents a deep learning framework based on the use of deep convolutional generative adversarial networks (DCGAN) for infrared face image super-resolution. We use DCGAN for upscaling the images by a factor of 4×4, starting at a size of 16×16 and obtaining a 64×64 face image. Tests are conducted using different infrared face datasets operating in the near-infrared (NIR) and the long-wave infrared (LWIR) spectrum. We can see that the proposed framework performs well and preserves important details of the face. This kind of approach can be very useful in security applications where we can scan faces in the crowd or detect faces at a distance and upscale them for further recognition through an infrared or a multispectral face recognition system.