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BACKGROUND: Computer-aided tongue and face diagnosis technology can make Traditional Chinese Medicine (TCM) more standardized, objective and quantified. However, many tongue images collected by the instrument may not meet the standard in clinical applications, which affects the subsequent quantitative analysis. The common tongue diagnosis instrument cannot determine whether the patient has fully extended the tongue or collected the face. OBJECTIVE: This paper proposes an image quality control algorithm based on deep learning to verify the eligibility of TCM tongue diagnosis images. METHODS: We firstly gathered enough images and categorized them into five states. Secondly, we preprocessed the training images. Thirdly, we built a ResNet34 model and trained it by the transfer learning method. Finally, we input the test images into the trained model and automatically filter out unqualified images and point out the reasons. RESULTS: Experimental results show that the model's quality control accuracy rate of the test dataset is as high as 97.06%. Our methods have the strong discriminative power of the learned representation. Compared with previous studies, it can guarantee subsequent tongue image processing. CONCLUSIONS: Our methods can guarantee the subsequent quantitative analysis of tongue shape, tongue state, tongue spirit, and facial complexion. [ABSTRACT FROM AUTHOR]

BACKGROUND: Stress is one of the critical health factors that could be detected by Human Activity Recognition (HAR) which consists of physical and mental health. HAR can raise awareness of self-care and prevent critical situations. Recently, HAR used non-invasive wearable physiological sensors. Moreover, deep learning techniques are becoming a significant tool for analyzing health data. OBJECTIVE: In this paper, we propose a human lifelog monitoring model for stress behavior recognition based on deep learning, which analyses stress levels during activity. The proposed approach considers activity and physiological data for recognizing physical activity and stress levels. METHODS: To tackle these issues, we proposed a model that utilizes hand-crafted feature generation techniques compatible with a Bidirectional Long Short-Term Memory (Bi-LSTM) based method for physical activity and stress level recognition. We have used a dataset called WESAD, collected using wearable sensors for model evaluation. This dataset presented four levels of stress emotion, including baseline, amusement, stress, and meditation. RESULTS: The following results are from the hand-crafted feature approaches compatible with the bidirectional LSTM model. The proposed model achieves an accuracy of 95.6% and an F1-score of 96.6%. CONCLUSION: The proposed HAR model efficiently recognizes stress levels and contributes to maintaining physical and mental well-being. [ABSTRACT FROM AUTHOR]

Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine the type of lymphoma by observing pathological images. At present, an automatic classification technology is urgently needed to assist doctors in analyzing the type of lymphoma. In this paper, by comparing the training results of the BP neural network and BP neural network optimized by genetic algorithm (GA-BP), adopts a deep residual neural network model (ResNet-50), with 374 lymphoma pathology images as the experimental data set. After preprocessing the dataset by image flipping, color transformation, and other data enhancement methods, the data set is input into the ResNet-50 network model, and finally classified by the softmax layer. The training results showed that the classification accuracy was 98.63%. By comparing the classification effect of GA-BP and BP neural network, the accuracy of the network model proposed in this paper is improved. The network model can provide an objective basis for doctors to diagnose lymphoma types. [ABSTRACT FROM AUTHOR]

Background: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection.Objective: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory.Methods: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 ∼ N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result.Results: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%.Conclusion: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction. [ABSTRACT FROM AUTHOR]

Background: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effective and accurate method for early detection. Due to the poor contrast, noise and artifacts which results in difficulty for radiologists to diagnose, Computer-Aided Diagnosis (CAD) systems are hence developed. The extraction of breast region is a fundamental and crucial preparation step for further development of CAD systems.Objective: The proposed method aims to extract breast region accurately from mammographic images where noise is suppressed, contrast is enhanced and pectoral muscle region is removed.Methods: This paper presents a new deep learning-based breast region extraction method that combines pre-processing methods containing noise suppression using median filter, contrast enhancement using CLAHE and semantic segmentation using Deeplab v3+ model.Results: The method is trained and evaluated on mini-MIAS dataset. It has also been evaluated on INbreast dataset. The results outperform those generated by other recent researches and are indicative of the capacity of the model to retain its accuracy and runtime advantage across different databases with different image resolutions.Conclusions: The proposed method shows state-of-the-art performance at extracting breast region from mammographic images. Wide range of evaluation on two commonly used mammography datasets proves the ability and adaptability of the method. [ABSTRACT FROM AUTHOR]

BACKGROUND: The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes. OBJECTIVE: In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise. METHODS: The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results. RESULTS: Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p < 0.05. CONCLUSION: The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images. [ABSTRACT FROM AUTHOR]

Background: Grape seed proanthocyanidin extract (GSPE) has a certain resistance to contrast light, which makes the boundary of the imaging image unclear.Objective: Because of this, an image processing algorithm is needed to process the contrast image to study the role of GSPE in the process of anti-ultraviolet.Methods: In this paper, the fuzzy edges of contrast images were processed by deep learning algorithm, and the changes of VEGF and PEDF expression in HaCaT cells before and after UVA irradiation and after GSPE intervention were studied.Results: The experiment results show that after processing, the edge and boundary of the image become clear and separable, which can be used to compare and analyze the test process. The image comparison results show that GSPE can down regulate the expression of VEGF gene and protein, and up regulate the expression of PEDF gene and protein. The synergistic effect of GSPE and GSPE can inhibit angiogenesis. It is confirmed that GSPE has the effect of anti-ultraviolet ray induced early angiogenesis. [ABSTRACT FROM AUTHOR]

Background: Medical patients can be diagnosed early, however it is difficult to extract effective features in medical image segmentation based on semantic information.Objective: A deep learning based image pixel block feature learning technology is studied in this paper.Methods: The unlabeled image block sample training stack noise reduction automatic encoder is used to learn and extract the deep features of the image, and construct the initial depth neural network model. The labeled samples are used to fine-tune the initial depth neural network model, the deep features of the image correspond to the category, and the depth neural network model with classification function is obtained. The model is used to classify the pixel block samples in the segmented image and detect the initial segmentation region of brain tumor tissue. Finally, threshold segmentation and morphological methods are used to optimize the initial results to obtain accurate segmentation results of brain tumor tissue.Results: The results show that this method can effectively improve the accuracy and sensitivity of segmentation. The running speed is also greatly improved compared with the traditional machine learning method. [ABSTRACT FROM AUTHOR]

Artificial neural networks have found applications in various areas of medical diagnosis. The capability of neural networks to learn medical data, mining useful and complex relationships that exist between attributes has earned it a major domain in decision support systems. This paper proposes a fast automatic system for the diagnosis of disk hernia and spondylolisthesis using biomechanical features and neural network. Such systems as described within this work allow the diagnosis of new cases using trained neural networks; patients are classified as either having disk hernia, spondylolisthesis, or normal. Generally, both disk hernia and spondylolisthesis present similar symptoms; hence, diagnosis is prone to inter-misclassification error. This work is significant in that the proposed systems are capable of making fast decisions on such somewhat difficult diagnoses with reasonable accuracies. Feedforward neural network and radial basis function networks are trained on data obtained from a public database. The results obtained within this research are promising and show that neural networks can find applications as efficient and effective expert systems for the diagnosis of disk hernia and spondylolisthesis. [ABSTRACT FROM AUTHOR]