Article
Engineering, Biomedical
Zheng Huang, Yiwen Zhao, Yunhui Liu, Guoli Song
Summary: In order to enhance the performance of brain tumor diagnosis, an adaptive multisequence fusing neural network (AMF-Net) is proposed to merge the characteristics of different MRI sequences. The experimental results show that the proposed network achieves average accuracies of 98.1% and 92.1%, indicating an improvement in performance for brain tumor diagnosis tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Information Systems
Tapan Kumar Das, Pradeep Kumar Roy, Mohy Uddin, Kathiravan Srinivasan, Chuan-Yu Chang, Shabbir Syed-Abdul
Summary: The study explores a new framework for diagnosing tumors using deep learning and deep convolutional neural networks. The model efficiently detects abnormalities and accurately predicts diseases in MRI data, achieving higher accuracy than recent peers. Further improvements could be made by designing models that reduce parameter space significantly without classification.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Oncology
Dusko Kozic, Nebojsa Lasica, Danica Grujicic, Savo Raicevic, Natasa Prvulovic Bunovic, Igor Nosek, Jasmina Boban
Summary: Non-enhancing brain metastatic tumors are rare, usually occurring after antiangiogenic treatment. This case report presents the first ever non-enhancing metastatic brain tumor without a prior history of antiangiogenic treatment, highlighting the importance of MRS analysis in atypical brain lesions.
FRONTIERS IN ONCOLOGY
(2022)
Article
Multidisciplinary Sciences
Jie Dong, Shujun Zhao, Yun Meng, Yong Zhang, Suxiao Li
Summary: This study aimed to explore the application value of a magnetic resonance imaging (MRI) image reconstruction model based on complex convolutional neural network (CCNN) in diagnosing and predicting the prognosis of cerebral infarction. Two new MRI image reconstruction models, D-FDRN and D-IDRN, were created by integrating two image reconstruction methods based on the CCNN algorithm. The study found that the PSNR and SSIM values of the MRI reconstructed image using the D-IDRN algorithm were higher than those of other algorithms, and analyzed the correlation between vein abnormality grading (VABG) and infarct size as well as the degree of stenosis of the responsible vessel. The changes in ADC value and DCavg value in the central area of the infarct could be used for diagnosing cerebral infarction.
Article
Chemistry, Multidisciplinary
Hiroto Yamashiro, Atsushi Teramoto, Kuniaki Saito, Hiroshi Fujita
Summary: The proposed grading pipeline combines cloud-based trained 3D CNN and original 3D CNN for early patient treatment and prognosis prediction. Through evaluation, the grading accuracy of all tumors using this automated method reaches 91.3%, comparable to multi-sequence methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Clinical Neurology
Xiuling Miao, Tianyu Shao, Yaming Wang, Qingjun Wang, Jing Han, Xinnan Li, Yuxin Li, Chenjing Sun, Junhai Wen, Jianguo Liu
Summary: This study aimed to evaluate the value of a CNN-based deep learning model in the differential diagnosis of space-occupying brain diseases on MRI. The results showed that the model had higher accuracy than senior neuroradiologists and could accurately identify radiographic features of tumefactive demyelinating lesions, tumefactive primary angiitis of the central nervous system, primary central nervous system lymphoma, and brain gliomas.
FRONTIERS IN NEUROLOGY
(2023)
Article
Biology
Qihang Ma, Siyuan Zhou, Chengye Li, Feng Liu, Yan Liu, Mingzheng Hou, Yi Zhang
Summary: In this paper, a novel brain tumor segmentation approach named DGRUnit is proposed, which includes a spatial reasoning module and a channel reasoning module to effectively model the long-range relationships and contextual interdependence in multimodal Magnetic Resonance images. Experimental results demonstrate the superior performance of the proposed method.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Jindong Sun, Yanjun Peng, Yanfei Guo, Dapeng Li
Summary: The study proposed a novel model based on deep learning for accurate segmentation of multimodal brain tissues from 3D medical images. The model achieved effective segmentation on two brain tumor segmentation datasets, demonstrating its potential as a powerful tool for studying medical images of brain tumors.
Article
Chemistry, Analytical
Imayanmosha Wahlang, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska
Summary: Effective classification techniques using deep learning on brain MRI images with added attributes of gender and age show improved accuracy in diagnosing brain tumors. Age and gender play a key role in classification, and the proposed technique outperforms existing methods in most cases.
Article
Biochemistry & Molecular Biology
Jayesh George Melekoodappattu, Chaithanya Kandambeth Puthiyapurayil, Anoop Vylala, Anto Sahaya Dhas
Summary: This manuscript presents an advanced approach that combines multimodal feature fusion and dual-path network. By leveraging pretrained models and a custom convolutional neural network, salient features are effectively extracted from the data using nonlinear mapping and expansive perception. The resulting two-stage ensemble hybrid CNN model achieves a high accuracy of 99.63% in brain tumor classification.
CELL BIOCHEMISTRY AND FUNCTION
(2023)
Article
Engineering, Biomedical
Hari Mohan Rai, Kalyan Chatterjee, Sergey Dashkevich
Summary: In this study, a deep convolutional neural network model was proposed for automatic detection and segmentation of brain tumors, achieving excellent performance. The model showed high accuracy and DICE scores when processing MRI images, as well as improvements in pixel quality on the decoder side.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Information Systems
Abdullah A. Asiri, Amna Iqbal, Javed Ferzund, Tariq Ali, Muhammad Aamir, Khalaf A. Alshamrani, Hassan A. Alshamrani, Fawaz F. Alqahtani, Muhammad Irfan, Ali H. D. Alshehri
Summary: This study proposes a ResNet-50 feature extractor based on a multilevel deep convolutional neural network for reliable brain tumor image segmentation. By classifying and detecting 2043 MRI patients, better average results are obtained compared to existing methods. This modified architecture could be an important tumor diagnosis system.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Biology
Ahmad Kazemi, Mohammad Ebrahim Shiri, Amir Sheikhahmadi, Mohamad Khodamoradi
Summary: Medical images play an important role in diagnosing diseases. A deep parallel convolutional neural network model consisting of AlexNet and VGGNet networks has achieved better results in medical image diagnosis and can serve as an effective decision support tool.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Multidisciplinary Sciences
Chun-Yao Lee, Chen-Hsu Hung
Summary: This article proposes a fault diagnosis system using feature ranking and differential evolution for feature selection in BLDC motors. The system achieved a high accuracy rate and robust anti-noise ability in experiments. Comparatively, it outperformed systems using discrete wavelet transform and various classifiers with higher accuracy and fewer features.
Article
Cell Biology
Si-Yuan Lu, Suresh Chandra Satapathy, Shui-Hua Wang, Yu-Dong Zhang
Summary: Brain tumors are a major cause of human mortality, with over 120 different types falling into primary and metastatic categories. Early detection of primary brain tumors is crucial, and this study presented a novel computer-aided diagnosis system, PBTNet, for accurately identifying primary brain tumors in MRI images. By utilizing a pre-trained ResNet-18 as the backbone model and three randomized neural networks as classifiers, the PBTNet demonstrated effectiveness in classification performance through 5-fold cross-validation.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)