A5100690719- Spectroscopy Techniques in Biomedical and Chemical Research
- Spectroscopy and Chemometric Analyses
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Advanced Chemical Sensor Technologies
- Infrared Thermography in Medicine
- Digital Imaging for Blood Diseases
- Metabolomics and Mass Spectrometry Studies
- Computational Drug Discovery Methods
- Identification and Quantification in Food
- Photoacoustic and Ultrasonic Imaging
- Traditional Chinese Medicine Studies
- Molecular Biology Techniques and Applications
- Anomaly Detection Techniques and Applications
- Cell Image Analysis Techniques
- Gene expression and cancer classification
- Rheumatoid Arthritis Research and Therapies
- Bee Products Chemical Analysis
- Systemic Lupus Erythematosus Research
- Advanced Graph Neural Networks
- Machine Learning in Materials Science
- RNA modifications and cancer
- Cutaneous Melanoma Detection and Management
- Brain Tumor Detection and Classification
- Gold and Silver Nanoparticles Synthesis and Applications
Xinjiang University
2016-2025
People's Hospital of Xinjiang Uygur Autonomous Region
2023-2024
Cloud Computing Center
2022-2024
Jiangsu University of Science and Technology
2024
Fuzhou University
2021
ORCID
2021
Utah State University
2021
Xinjiang Medical University
2020
First Affiliated Hospital of Xinjiang Medical University
2020
Wuhan University of Technology
2019
Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination gold standard for qualitative and clinical staging lung tumors. However, process doctors to examine thousands histopathological images very cumbersome, especially with less experience. Therefore, objective pathological diagnosis results can effectively help choose appropriate treatment mode, thereby improving survival rate patients. For current problem incomplete experimental subjects...
Abstract We describe a new supervised learning‐based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by user building statistical model that captures the texture and shape variations of nuclear structures given dataset to be segmented. Segmentation subsequent, unlabeled, images is then performed finding instance best matches (in normalized cross correlation sense) local neighborhood in input image. demonstrate application our...
Abstract Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, very popular. This study proposes a method Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated honey. In this study, honey collected by local beekeepers was selected for adulteration detection. The spectral data compressed Savitzky–Golay smoothing and partial least squares (PLS)...
Abstract Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, diagnosis by highly subjective and prone misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel spatial mechanism, developed computer-aided (CAD) method CNN MCCBAM, called HCCANet. 630 histopathology images processed with Gaussian filtering denoising were included gradient-weighted class...
Abstract Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity strong stability was prepared by immersion plating heat treatment using porous (PSB) the substrate. The combines five deep learning algorithms of improved AlexNet, ResNet,...
Abstract The spectral fusion by Raman spectroscopy and Fourier infrared combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, finds the segment highest sensitivity further advance diagnosis speed. Compared single or spectroscopy, proposal can improve detection accuracy, obtain more features, indicating greater differences between normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature...
Abstract Lung cancer and glioma are common malignancies worldwide pose a serious threat to human health. There may be certain correlation between lung patients in serum composition, but date, no study on the classification of is available. In this paper, differences relationships were analyzed from Raman spectra. The existing detection methods time consuming expensive, so we propose method based patient spectra combined with deep learning, which can screen accurately speed low cost. study,...
Abstract Medical diagnosis technology based on convolutional neural networks (CNNs) has achieved good performance. In this study, we collected serum samples from 38 glioma patients and 45 healthy controls used partial least squares (PLS) analysis to reduce the dimension of data. Different levels noise were added reduced data onto augmentation, AlexNet, ResNet, GoogLeNet fine‐tuning models applied for classification. To evaluate performance models, five‐fold cross‐validation. The accuracy...