OPUS Collection:

Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology.

2022-01-01T00:00:00Z

Title: Chronic Cervicitis and Cervical Cancer Detection Based on Deep Learning of Colposcopy Images Toward Translational Pharmacology. Authors: Huang, W; Sun, S; Yu, Z; Lu, S; Feng, H Abstract: With the rapid development of deep learning, automatic image recognition is widely used in medical development. In this study, a deep learning convolutional neural network model was developed to recognize and classify chronic cervicitis and cervical cancer. A total of 10,012 colposcopy images of 1,081 patients from Hunan Provincial People's Hospital in China were recorded. Five different colposcopy image features of the cervix including chronic cervicitis, intraepithelial lesions, cancer, polypus, and free hyperplastic squamous epithelial tissue were extracted to be applied in our deep learning network convolutional neural network model. However, the result showed a low accuracy (42.16%) due to computer misrecognition of chronic cervicitis, intraepithelial lesions, and free hyperplastic squamous epithelial tissue with high similarity. To optimize this model, we selected two significant feature images: chronic cervicitis and cervical cancer to input into a deep learning network. The result indicates high accuracy and robustness with an accuracy of 95.19%, which can be applied to detect whether the patient has chronic cervicitis or cervical cancer based on the patient's colposcopy images.

A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model

2021-01-01T00:00:00Z

Title: A Novel Machine Learning-Based Systolic Blood Pressure Predicting Model Authors: Zheng, J; Yu, Z Abstract: Blood pressure (BP) is a vital biomedical feature for diagnosing hypertension and cardiovascular diseases. Traditionally, it is measured by cuff-based equipment, e.g., sphygmomanometer; the measurement is discontinued and uncomfortable. A cuff-less method based on different signals, electrocardiogram (ECG) and photoplethysmography (PPG), is proposed recently. However, this method is costly and inconvenient due to the collections of multisensors. In this paper, a novel machine learning-based systolic blood pressure (SBP) predicting model is proposed. The model was evaluated by clinical and lifestyle features (gender, marital status, smoking status, age, weight, etc.). Different machine learning algorithms and different percentage of training, validation, and testing were evaluated to optimize the model accuracy. Results were validated to increase the accuracy and robustness of the model. The performance of our model met both the level of grade A (British Hypertension Society (BHS) standard) and the American National Standard from the Association for the Advancement of Medical Instrumentation (AAMI) for SBP estimation.

A Supervised ML Applied Classification Model for Brain Tumors MRI.

2022-01-01T00:00:00Z

Title: A Supervised ML Applied Classification Model for Brain Tumors MRI. Authors: Yu, Z; He, Q; Yang, J; Luo, M Abstract: Brain Tumor originates from abnormal cells, which is developed uncontrollably. Magnetic resonance imaging (MRI) is developed to generate high-quality images and provide extensive medical research information. The machine learning algorithms can improve the diagnostic value of MRI to obtain automation and accurate classification of MRI. In this research, we propose a supervised machine learning applied training and testing model to classify and analyze the features of brain tumors MRI in the performance of accuracy, precision, sensitivity and F1 score. The result presents that more than 95% accuracy is obtained in this model. It can be used to classify features more accurate than other existing methods.

Controlled Release of Curcumin from HPMC (Hydroxypropyl Methyl Cellulose) Co-Spray-Dried Materials.

2021-01-01T00:00:00Z

Title: Controlled Release of Curcumin from HPMC (Hydroxypropyl Methyl Cellulose) Co-Spray-Dried Materials. Authors: Zheng, J; Wang, B; Xiang, J; Yu, Z Abstract: In order to achieve the controlled release of curcumin, HPMC (hydroxypropyl methyl cellulose) was spray dried with curcumin and lactose. The spray-dried materials were pressed into tablets with a diameter of 8 mm, and their release characteristics in vitro were measured. In vitro experiments showed that the release of curcumin from the HPMC mixture was significantly slower due to the sustained-release property of HPMC as a typical excipient. The release profile of curcumin from the HPMC mixture was relatively stable for a controlled release. SEM images show that the HPMC co-spray-dried powders have crumpled surfaces due to the large molecular weight of HPMC. DSC, XRD, FTIR, N₂ adsorption, and TGA have been measured for the spray-dried curcumin materials. This work indicates that HPMC can be used as a controlled-release excipient for curcumin preparations.