It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. ...A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.
COVID-19 is a rapidly spreading global threat that has been declared as a pandemic by the WHO. COVID-19 is transmitted via droplets or direct contact and infects the respiratory tract resulting in ...pneumonia in most of the cases and acute respiratory distress syndrome (ARDS) in about 15 % of the cases. Mortality in COVID-19 patients has been linked to the presence of the so-called "cytokine storm" induced by the virus. Excessive production of proinflammatory cytokines leads to ARDS aggravation and widespread tissue damage resulting in multi-organ failure and death. Targeting cytokines during the management of COVID-19 patients could improve survival rates and reduce mortality.
Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep convolutional neural networks (DCNN) are currently the main tool used for image analysis and classification purposes. ...There are several DCNN architectures among them AlexNet, GoogleNet, and residual networks (ResNet).
This paper presents a new computer-aided diagnosis (CAD) system based on feature extraction and classification using DL techniques to help radiologists to classify breast cancer lesions in mammograms. This is performed by four different experiments to determine the optimum approach. The first one consists of end-to-end pre-trained fine-tuned DCNN networks. In the second one, the deep features of the DCNNs are extracted and fed to a support vector machine (SVM) classifier with different kernel functions. The third experiment performs deep features fusion to demonstrate that combining deep features will enhance the accuracy of the SVM classifiers. Finally, in the fourth experiment, principal component analysis (PCA) is introduced to reduce the large feature vector produced in feature fusion and to decrease the computational cost. The experiments are performed on two datasets (1) the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) and (2) the mammographic image analysis society digital mammogram database (MIAS).
The accuracy achieved using deep features fusion for both datasets proved to be the highest compared to the state-of-the-art CAD systems. Conversely, when applying the PCA on the feature fusion sets, the accuracy did not improve; however, the computational cost decreased as the execution time decreased.
•Proposing a multi-DCNN based CAD system for breast cancer classification.•Deep features fusion had an influence on the classification accuracy.•Searching for the best combination of deep features.•Selecting the number of principal components with a sequential forward method.•The computer-aided diagnosis system proposed proved to be reliable.
Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening ...worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system's design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.
The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic ...virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.
Worldwide, the COVID-19 epidemic, which started in 2019, has resulted in millions of deaths. The medical research community has widely used computer analysis of medical data during the pandemic, ...specifically deep learning models. Deploying models on devices with constrained resources is a significant challenge due to the increased storage demands associated with larger deep learning models. Accordingly, in this paper, we propose a novel compression strategy that compresses deep features with a compression ratio of 10 to 90% to accurately classify the COVID-19 and non-COVID-19 computed tomography scans. Additionally, we extensively validated the compression using various available deep learning methods to extract the most suitable features from different models. Finally, the suggested DeepCSFusion model compresses the extracted features and applies fusion to achieve the highest classification accuracy with fewer features. The proposed DeepCSFusion model was validated on the publicly available dataset “SARS-CoV-2 CT” scans composed of 1252 CT. This study demonstrates that the proposed DeepCSFusion reduced the computational time with an overall accuracy of 99.3%. Also, it outperforms state-of-the-art pipelines in terms of various classification measures.
Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the ...contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.
To investigate the effect of kangaroo care (KC) and its duration on neurobehavioral performance, stress response, breastfeeding success, and vital signs in premature infants.
One hundred and twenty ...premature infants were randomized to receive either KC for 60 min daily, KC for 120 min daily or conventional care (controls) for at least 7 days. Salivary cortisol was measured before and after the first KC session and then after 7 days. Temperature, respiration rate, heart rate, and oxygen saturation were recorded, before and after KC. Neonates were evaluated by the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS).
Both KC groups demonstrated higher scores for attention, arousal, regulation, nonoptimal reflexes, and quality of movements and lower scores for handling, excitability, and lethargy, compared to controls (p < 0.05). Both KC groups had higher infant breastfeeding assessment tool score and reached full enteral feeds faster than controls (p < 0.05). After the first KC session, improvement in O
saturation and temperature was observed in KC 120-min group compared with the KC 60-min group (p < 0.05). Salivary cortisol decreased in both KC groups compared with controls after 7 days (p < 0.05).
Preterm neonates who receive KC for long durations reach full enteral feeds faster, have better breastfeeding success, neurobehavioral performance, thermal control, and tissue oxygenation.
Background
IL-37 is an anti-inflammatory cytokine that increases in several inflammatory diseases with the main inducing signal for its production being pro-inflammatory cytokines like TNF-α. We ...aimed to assess the correlation between peritoneal fluid levels of IL-37 and TNF-α in endometriosis patients and investigate their association with disease stage. Levels of IL-37 and TNF-α were assessed in peritoneal fluid of 50 patients with endometriosis and 23 endometriosis-free females using enzyme-linked immunosorbent assay. We also assessed serum levels of IL-37 using enzyme-linked immunosorbent assay and expression of IL-37 mRNA in peritoneal fluid cells using polymerase chain reaction.
Results
Peritoneal fluid levels of IL-37 and TNF-α were higher in endometriosis patients than in control females. Also, levels were higher in patients with late endometriosis than patients with early endometriosis. In addition, serum levels of IL-37, as well as IL-37 mRNA expression in peritoneal fluid cells, were higher in patients than controls. In endometriosis patients, peritoneal fluid levels of IL-37 positively correlated with levels of TNF-α.
Conclusion
Collectively, our results show increased levels of the anti-inflammatory cytokine IL-37 in endometriosis patients that correlate with levels of the pro-inflammatory cytokine TNF-α, one of the main signals for IL-37 production.
To examine the cross‐talk between NK cells and DCs in hepatitis C virus (HCV) infection, we isolated monocytes and NK cells from 20 chronic HCV patients and 20 healthy controls. Monocytes were used ...to generate immature DCs which were pulsed with HCV peptides (core, NS3–NS4, and NS5). Four different cocultures were carried out: E1, both DCs and NK cells were from a chronic HCV patient; E2, NK cells from a healthy control cocultured with DCs from a chronic HCV patient; E3, NK cells from a chronic HCV patient cocultured with DCs from a healthy control; and E4, both DCs and NK cells were from a healthy control. Using flow cytometry, we assessed the effect of these different cocultures on levels of maturation markers on DCs and levels of activation/inhibition markers on NK cells. Results showed that peptide‐pulsed HCV DCs showed a maturation defect in the form of decreased HLA‐DR, decreased CD86, and increased CD83 expression especially when cocultured with HCV NK. This was mainly due to core peptide pulsing and to a lesser extent due to NS5 pulsing, whereas there was no effect with NS3–NS4 pulsing. Alternatively, HCV NK cells upregulated both activation and inhibition markers especially when cocultured with healthy DCs. Compared with E2, E1 resulted in higher apoptosis of both NK cells and DCs with the percentage of NK apoptosis higher than that of DCs. Taken together, the data indicate that HCV infection impairs NK–DC cross‐talk which may be a leading cause in viral persistence and chronicity.
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