Convolutional Neural Network For Diagnosis Research Articles

Regional-asymmetric Adaptive Graph Convolutional Neural Network for Diagnosis of Autism in Children with Resting-state EEG.

Currently, resting-state electroencephalography (rs-EEG) has become an effective and low-cost evaluation way to identify autism spectrum disorders (ASD) in children. However, it is of great challenge to extract useful features from raw rs-EEG data to improve diagnosis performance. Traditional methods mainly rely on the design of manual feature extractors and classifiers, which are separately performed and cannot be optimized simultaneously. To this end, this paper proposes a new end-to-end diagnostic method based on a recently emerged graph convolutional neural network for the diagnosis of ASD in children. Inspired by related neuroscience findings on the abnormal brain functional connectivity and hemispheric asymmetry characteristics observed in autism patients, we design a new Regional-asymmetric Adaptive Graph Convolutional Neural Network (RAGNN). It utilizes a hierarchical feature extraction and fusion process to learn separable spatiotemporal EEG features from different brain regions, two hemispheres, and a global brain. In the temporal feature extraction section, we utilize a convolutional layer that spans from the brain area to the hemisphere. This allows for effectively capturing temporal features both within and between brain areas. To better capture spatial characteristics of multi-channel EEG signals, we employ adaptive graph convolutional learning to capture non-Euclidean features within the brain's hemispheres. Additionally, an attention layer is introduced to highlight different contributions of the left and right hemispheres, and the fused features are used for classification. We conducted a subject-independent cross-validation experiment on rs-EEG data from 45 children with ASD and 45 typically developing (TD) children. Experimental results have shown that our proposed RAGNN model outperformed several existing deep learning-based methods (ShaollowNet, EEGNet, TSception, ST-GCN, and CGRU-MDGN).

Retraction: Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction.

Retraction: Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction.

Electrochemical impedance spectroscopy image transformation-based convolutional neural network for diagnosis of external environment classification affecting abnormal aging of Li-ion batteries

Various electric mobilities are being developed that use Lithium (Li)-ion batteries as the primary power source for target applications. Especially, with the widespread adoption of electric vehicle (EV), the significance of battery management system (BMS) which are closely related to the stable operation of Li-ion batteries, is also increasing. Diagnosing the external environment is essential because the aging pattern of the battery varies depending on the environment condition in which the battery is exposed and can cause abnormal failures. This study presents an approach for classifying the external environment using electrochemical impedance spectroscopy (EIS) as an indicator, which is closely related to the internal chemical characteristics of the batteries. Recurrence plot (RP) algorithm is applied to improve the performance of convolution neural network (CNN) used as classification model as well as original EIS image. According to the frequency at which the main parameters of the Randles circuit model are derived, an additional dataset is constructed based on the selected 3-points. This paper presents the results of the classification from the external environment based on the three methods and derives a statistical evaluation metric to prove the stability and performance of the model. Moreover, it is also suggested that the appropriate method can be selected based on the desired cost by deriving the detailed model loss and accuracy for the training epoch, which is a trade-off relationship for each method.

A Novel Deep Convolutional Neural Network for Diagnosis of Skin Disease

Due to its intricacy, dermatology presents the most challenging and uncertain terrain for diagnosis. Skin conditions like Carcinoma and Melanoma are frequently very challenging to identify in the early stages and are much more challenging to define independently. The use of pattern recognition models to automate detection has been studied by a number of writers. This research describes a novel Deep Convolutional Neural Network (DCNN) for Skin Disease Detection. The photographs of skin would undergo processing to remove unwanted noise as well as to improve the photos. The performance of classification will be greatly impacted by the pixel values of a picture. The picture is classified using the softmax classifier method by feature extraction utilising DCCN, and a diagnosis report is produced as the result. In comparison to more classic approaches like KNN (K-Nearest Neighbour) and CNN, this methodology will provide results faster and with improved accuracy, precision, and recall. With a detection time of 10,000 milliseconds, DCNN achieved accuracy, precision, and recall percentages of 98.4%, 96.3%, and 97.2%, respectively.

Application of Convolutional Neural Networks for Diagnosis of Eosinophilic Esophagitis Based on Endoscopic Imaging.

Subjective symptoms associated with eosinophilic esophagitis (EoE), such as dysphagia, are not specific, thus the endoscopic identification of suggestive EoE findings is quite important for facilitating endoscopic biopsy sampling. However, poor inter-observer agreement among endoscopists regarding diagnosis has become a complicated issue, especially with inexperienced practitioners. Therefore, we constructed a computer-assisted diagnosis (CAD) system using a convolutional neural network (CNN) and evaluated its performance as a diagnostic utility. A CNN-based CAD system was developed based on ResNet50 architecture. The CNN was trained using a total of 1192 characteristic endoscopic images of 108 patients histologically proven to be in an active phase of EoE (≥15 eosinophils per high power field) as well as 1192 normal esophagus images. To evaluate diagnostic accuracy, an independent test set of 756 endoscopic images from 35 patients with EoE and 96 subjects with a normal esophagus was examined with the constructed CNN. The CNN correctly diagnosed EoE in 94.7% using a diagnosis per image analysis, with an overall sensitivity of 90.8% and specificity of 96.6%. For each case, the CNN correctly diagnosed 37 of 39 EoE cases with overall sensitivity and specificity of 94.9% and 99.0%, respectively. These findings indicate the usefulness of CNN for diagnosing EoE, especially for aiding inexperienced endoscopists during medical check-up screening.

DGCNN: deep convolutional generative adversarial network based convolutional neural network for diagnosis of COVID-19.

The latest threat to global health is the coronavirus disease 2019 (COVID-19) pandemic. To prevent COVID-19, recognizing and isolating the infected patients is an essential step. The primary diagnosis method is Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, the sensitivity of this test is not satisfactory to successfully control the COVID-19 outbreak. Although there exist many datasets of chest X-rays (CXR) images, but few COVID-19 CXRs are presently accessible owing to privacy of patients. Thus, many researchers have utilized data augmentation techniques to augment the datasets. But, it may cause over-fitting issues, as the existing data augmentation techniques include small modifications to CXRs. Therefore, in this paper, an efficient deep convolutional generative adversarial network and convolutional neural network (DGCNN) is designed to diagnose COVID-19 suspected subjects. Deep convolutional generative adversarial network (DGAN) consists of two networks trained adversarially such that one generates fake images and the other differentiates between them. Thereafter, convolutional neural network (CNN) is utilized for classification purpose. Extensive experiments are conducted to evaluate the performance of the proposed DGCNN. Performance analysis demonstrates that DGCNN can highly improves the diagnosis performance.

Deep learning convolutional neural network in diagnosis of serous effusion in patients with malignant tumor by tomography

Deep learning convolutional neural network in diagnosis of serous effusion in patients with malignant tumor by tomography

Magnetic resonance imaging reconstruction algorithm under complex convolutional neural network in diagnosis and prognosis of cerebral infarction.

This study was to explore the application value of magnetic resonance imaging (MRI) image reconstruction model based on complex convolutional neural network (CCNN) in the diagnosis and prognosis of cerebral infarction. Two image reconstruction methods, frequency domain reconstruction network (FDRN) and image domain reconstruction network (IDRN), were introduced based on the CCNN algorithm. In addition, they were integrated to form two new MRI image reconstruction models, namely D-FDRN and D-IDRN. The peak signal to noise ratio (PSNR) value and structural similarity index measure (SSIM) value of the image were compared and analyzed before and after the integration. The MRI images of patients with cerebral infarction in the dataset were undertaken as the data source, the average diffusion coefficient (DCavg) and apparent diffusion coefficient (ADC) values of different parts of the MRI image were measured, respectively. The correlation of the vein abnormality grading (VABG) to the infarct size and the degree of stenosis of the responsible vessel was analyzed in this study. The results showed that the PSNR and SSIM values of the MRI reconstructed image of the D-IDRN algorithm based on the CCNN algorithm in this study were higher than those of other algorithms. There was a positive correlation between the VABG and the infarct size (r = 0.48 and P = 0.002), and there was a positive correlation between the VABG the degree of stenosis of the responsible vessel (r = 0.58 and P < 0.0001). The ADC value of the central area of the infarct on the affected side was significantly greatly lower than that of the normal side (P < 0.01), and the DCavg value of the central area of the infarct was much lower in contrast to the normal side (P < 0.05). It indicated that an image reconstruction algorithm constructed in this study could improve the quality of MRI images. The ADC value and DCavg value changed in the infarct central area could be used as the basis for the diagnosis of cerebral infarction. If the vein was abnormal, the patient suffered from severe vascular stenosis, large infarction area, and poorer prognosis.

Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.

Reverse‐Transcription Polymerase Chain Reaction (RT‐PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID‐19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x‐ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X‐ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross‐validation was carried out. The performance of the model using overall dataset was compared with the means of cross‐validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD). MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2). ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment. ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.

Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Background and AimMagnifying endoscopy with narrow‐band imaging (ME‐NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME‐NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI‐assisted CNN computer‐aided diagnosis (CAD) system, based on ME‐NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI‐assisted CNN‐CAD system.MethodsThe AI‐assisted CNN‐CAD system (ResNet50) was trained and validated on a dataset of 5574 ME‐NBI images (3797 EGCs, 1777 non‐cancerous mucosa and lesions). To evaluate the diagnostic accuracy, a separate test dataset of 2300 ME‐NBI images (1430 EGCs, 870 non‐cancerous mucosa and lesions) was assessed using the AI‐assisted CNN‐CAD system.ResultsThe AI‐assisted CNN‐CAD system required 60 s to analyze 2300 test images. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 98.7%, 98%, 100%, 100%, and 96.8%, respectively. All misdiagnosed images of EGCs were of low‐quality or of superficially depressed and intestinal‐type intramucosal cancers that were difficult to distinguish from gastritis, even by experienced endoscopists.ConclusionsThe AI‐assisted CNN‐CAD system for ME‐NBI diagnosis of EGC could process many stored ME‐NBI images in a short period of time and had a high diagnostic ability. This system may have great potential for future application to real clinical settings, which could facilitate ME‐NBI diagnosis of EGC in practice.

Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer.

Diagnosis and evaluation of early gastric cancer (EGC) using endoscopic images is significantly important; however, it has some limitations. In several studies, the application of convolutional neural network (CNN) greatly enhanced the effectiveness of endoscopy. To maximize clinical usefulness, it is important to determine the optimal method of applying CNN for each organ and disease. Lesion-based CNN is a type of deep learning model designed to learn the entire lesion from endoscopic images. This review describes the application of lesion-based CNN technology in diagnosis of EGC.

Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging.

Ultrasound imaging (US) is commonly used in nephrology for diagnostic studies of the kidneys and lower urinary tract. However, it remains challenging to automate the disease diagnosis based on clinical 2D US images since they provide partial anatomic information of the kidney and the 2D images of the same kidney may have heterogeneous appearance. To overcome this challenge, we develop a novel multi-instance deep learning method to build a robust classifier by treating multiple 2D US images of each individual subject as multiple instances of one bag. Particularly, we adopt convolutional neural networks (CNNs) to learn instance-level features from 2D US kidney images and graph convolutional networks (GCNs) to further optimize the instance-level features by exploring potential correlation among instances of the same bag. We also adopt a gated attention-based MIL pooling to learn bag-level features using full-connected neural networks (FCNs). Finally, we integrate both instance-level and bag-level supervision to further improve the bag-level classification accuracy. Ablation studies and comparison results have demonstrated that our method could accurately diagnose kidney diseases using ultrasound imaging, with better performance than alternative state-of-the-art multi-instance deep learning methods.