Colorectal cancer (CRC) is a common malignant tumor that seriously threatens human health. CRC presents a formidable challenge in terms of accurate identification due to its indistinct boundaries. With the widespread adoption of convolutional neural networks (CNNs) in image processing, leveraging CNNs for automatic classification and segmentation holds immense potential for enhancing the efficiency of colorectal cancer recognition and reducing treatment costs. This paper explores the imperative necessity for applying CNNs in clinical diagnosis of CRC. It provides an elaborate overview on research advancements pertaining to CNNs and their improved models in CRC classification and segmentation. Furthermore, this work summarizes the ideas and common methods for optimizing network performance and discusses the challenges faced by CNNs as well as future development trends in their application towards CRC classification and segmentation, thereby promoting their utilization within clinical diagnosis.
Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.
Objective To retrospectively analyze and classify 23 open fractures that resulted in severe infection, in order to provide evidence that can be used in future disaster scenarios. Methods Based on medical records of 23 cases of open fracture and subsequent bacterial infection, we analyzed the clinical diagnosis, treatment, laboratory tests, bacterial smear of wound secretion, and the bacterial culture of the wound secretion. We then analyzed which antimicrobial agents were used and how they were applied, and the subsequent effect on controlling the serious infection.? Results All cases were related to seismic injury and belonged to class VI open fracture. Eight cases were male and 15 were female. All cases had similar symptoms such as chills, fever, large scale muscle necrosis, and severe infection. A direct smear of the wound showed that the number of cases with one bacterial infection was 6 (26.09%), the number that had double bacterial infections was 12 (52.18%), and the number with multiple bacterial infections was 5 (21.74%).There were 18 strains of 11 types of bacteria recovered from wound samples. Conclusion Early treatment with the joint application of multiple antibacterial agents, early debridement, and adequate drainage all helped to control the infection and avoid nosocomial infection. Employing these strategies in the future will control infection in disaster situations.
To solve the complex interaction problems of hepatitis disease classification, we proposed a lasso method (least absolute shrinkage and selection operator method) with feature interaction. First, lasso penalized function and hierarchical convex constraint were added to the interactive model which is newly defined. Then the model was solved with the convex optimal method combining Karush-Kuhn-Tucker (KKT) condition with generalized gradient descent. Finally, the sparse solution of the main effect features and interactive features were derived, and the classification model was implemented. The experiments were performed on two liver data sets and proved that features interaction contributed to the classification of liver diseases. The experimental results showed that the feature interaction lasso method was of strong explanatory ability, and its effectiveness and efficiency were superior to those of lasso, of all pair-wise lasso, support vector machine (SVM) method, K nearest neighbor (KNN) method, linear discriminant analysis (LDA) classification method, etc.
Objective To investigate the mass casualty triage system and its application, to provide evidence and advice for its future standardized use. Method Based on the principles and methods of systematic reviews, we searched MEDLINE (1950 to 2008), The Cochrane library (Issue 2, 2008) and CBM (from establishment to May 2008) to identify papers written in English of Chinese which described mass casualty triage systems or triage systems specific to the aftermath of earthquakes. We extracted information on name, grades, criteria, main characteristics and application of each triage system from the papers involving mass casualty triage systems. We also extracted information on setting, personnel performing the triage, grades, and characteristics from those papers describing any specific triage system for earthquake. We compared the colour of tags, codes and other materials used in different triage systems. Result We included 38 English and 6 Chinese papers. For mass casualty triage systems, we identified 7 primary triage methods with 4 grades.Three of these had relevant application reports. There were 6 secondary triage methods with 3-5 grades, and none had relevant application reports. Four tag methods were identified. Seven papers, 2 of which were published in China, reported specific secondary triage methods for earthquakes. Conclusion Based on the current evidence, there is no universally accepted mass casualty triage system with documented reliability and validity. No triage system has been developed specifically for the wounded in earthquakes. There are large differences between the triage methods for earthquake and other mass casualty incidents. Future research should focus on the development of a reliable and valid mass casualty triage system, aimed at maximizing the capacity for medical rescue.
Stem cells belong to a subgroup of undifferentiated cells in organisms, which has the features of proliferation, self maintaining, and self renewal, and may produce plentiful filial generation with functions. According to the researches on embryonic stem cells, retinal stem cells in adults, and intraocular tumor stem cells, stems cells exist in human embryo, adult retina, and also intraocular tumors like retinoblastoma and choroidal melanoma. Different stem cells transplanted into subretinal interspace or vitreous cavity may differentiate into structure of neurone or retina. Stem cells may become a newest target of the researches on pathogenesis and treatment of diseases. (Chin J Ocul Fundus Dis, 2007, 23: 83-86)
One hundred and thirty patients with uveitis in north-western zone of our country were analyzed based on anatomical classification and their causes. It was found that anterior uveitis was the commonest type in uveitis,accounting for 86.15% of total patients. Intermediate uveitis, pan-uveitis and posterior uveitis accounted repectively for 6.92%, 3.85%and3.08% of the total patients. Rheumatic arthritis was the most frequently accompanied systemic disease in patients with uveitis,showing a possibly causative link between them in their pathogenesis. （Chin J Ocul Fundus Dis,1994,10:156-158）
Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.