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MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis [article]

Mo Zhang, Quanzheng Li
2020 arXiv   pre-print
The new method, named multi-scale graph wavelet neural network (MS-GWNN), leverages the localization property of spectral graph wavelet to perform multi-scale analysis.  ...  In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer.  ...  Spectral Graph Wavelet Transform Spectral graph wavelet transform was originally proposed by Hammond et al.  ... 
arXiv:2012.14619v1 fatcat:tc5xnimmdzbl7k3tvziefskcte

ENCRYPTION BASED WATERMARKING TECHNIQUE FOR SECURITY OF MEDICAL IMAGE

Abderrahmane Daham, Mohamed Ouslim
2022 Zenodo  
In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image.  ...  This paper proposes an encryption-based image watermarking scheme for medical images using a customized quantization of wavelet coefficient and a crypto system based on the chaotic cipher of Singular Value  ...  PLIMINARIES Discrete Wavelet Transform (DWT) DWT has excellent spatial localization and multi-resolution characteristics, which are similar to the theoretical models of the human visual system [15]  ... 
doi:10.5281/zenodo.6378802 fatcat:dbaiahbasncpxchjg6qhx6xkki

Identification of Pancreaticoduodenectomy Resection for Pancreatic Head Adenocarcinoma: A Preliminary Study of Radiomics

Bei Hui, Jia-Jun Qiu, Jin-Heng Liu, Neng-Wen Ke
2020 Computational and Mathematical Methods in Medicine  
To reduce overfitting, the SVM classifier embedded a linear kernel and adopted the leave-one-out cross-validation. Results.  ...  regions; (iii) enhance the textures of the fitted ROIs combining wavelet transform and fractional differential; (iv) extract texture features from the enhanced ROIs combining wavelet transform and statistical  ...  It consists of five stages: Stage 1: obtain ROIs (regions of interest) by preprocessing patients' CT images Stage 2: by solving discrete Laplacian equations with Dirichlet boundary conditions, fit the  ... 
doi:10.1155/2020/2761627 pmid:32377222 pmcid:PMC7182967 fatcat:qfrtxzlryjgcvb4mriimy7bwgy

A REVIEW STUDY OF METHODS UTILIZED FOR IDENTIFYING AND SEGMENTING THE BRAIN TUMOR FROM MR IMAGERIES

MOHD SHAFRY MOHD RAHIM AHMED SAIFULLAH SAMI
2019 Zenodo  
This paper provides a detailed analysis of the existent methods and approaches utilized in medical image segmentation.  ...  the stipulated features., [1]maintains that the utilization of a computer-aided diagnosis in medical imaging influences the decision-making capacity of specialists in the provision of accurate images  ...  Locality was considered in defining whether local anchor embedding was more applicable in solving linear projection weights compared with other coding methods.  ... 
doi:10.5281/zenodo.3256441 fatcat:xiqd75juvbbhnjbwffgruujnbi

An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue

Jelena Musulin, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić, Zlatan Car
2021 Cancers  
Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually  ...  In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images  ...  Acknowledgments: This research has been (partly) supported by the CEEPUS network CIII-HR-0108, European Regional Development Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/cancers13081784 pmid:33917952 pmcid:PMC8068326 fatcat:2ebrjp2ttzeybkffcvyvjm2rfe

Application of Deep Learning and WT-SST in Localization of Epileptogenic Zone Using Epileptic EEG Signals

Sani Saminu, Guizhi Xu, Zhang Shuai, Isselmou Abd El Kader, Adamu Halilu Jabire, Yusuf Kola Ahmed, Ibrahim Abdullahi Karaye, Isah Salim Ahmad
2022 Applied Sciences  
Discrete Wavelet Transform with Deep Learning Network (DWT-DNN) (3). Combined hybrid features with DNN (Hybrid-DNN) as an optimized DNN model. Lastly, (4).  ...  A newly proposed technique using Wavelet Synchrosqueezing Transform-Deep Convolutional Neural Network (WTSST-DCNN).  ...  The reallocation of coefficients results from a discrete-time continuous wavelet transform to obtain a compact time-frequency representation. tinuous The continuous wavelet transform can be defined as  ... 
doi:10.3390/app12104879 fatcat:d752224fcbde3l3d3lr6qrhuby

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Mabrook S. Al-Rakhami, Abdu Gumaei
2021 IEEE Access  
This model outperforms any previous works using the same database by a considerable margin.  ...  The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning.  ...  Among the feature extraction techniques, fast Fourier transform (FFT), short Fourier transform (STFT), discrete wavelet transform (DWT), continuous wavelet transform (CWT), Q wavelet transform (TQWT),  ... 
doi:10.1109/access.2021.3063129 fatcat:io5rva7lnnay5hkuft6sllqkz4

A Survey on Contemporary Computer-Aided Tumor, Polyp, and Ulcer Detection Methods in Wireless Capsule Endoscopy Imaging [article]

Tariq Rahim, Muhammad Arslan Usman, Soo Young Shin
2019 arXiv   pre-print
Wireless capsule endoscopy (WCE) is a process in which a patient swallows a camera-embedded pill-shaped device that passes through the gastrointestinal (GI) tract, captures and transmits images to an external  ...  In this paper, we have presented a survey of contemporary computer-aided detection methods that take WCE images as input and classify those images in a diseased/abnormal or disease-free/ normal image.  ...  For the multi-resolution analysis, transform like the 2-D discrete wavelet transform, dual-tree complex wavelet transforms (DTCWT), Gabor wavelet transforms, and curvelet transforms is exploited.  ... 
arXiv:1910.00265v1 fatcat:cziq6sauuzaqpgpmca5pmgdwke

Cell image classification: a comparative overview [article]

Mohammad Shifat-E-Rabbi, Xuwang Yin, Cailey Elizabeth Fitzgerald, Gustavo K. Rohde
2022 arXiv   pre-print
Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis  ...  of cancer from images acquired using cytological and histological techniques.  ...  Acknowledgements This work was supported in part by National Institutes of Health awards GM130825 and GM090033.  ... 
arXiv:1906.03316v2 fatcat:45icigrv5zhgxa3afs62hyjrki

Using Machine Learning to Automate Mammogram Images Analysis [article]

Xuejiao Tang, Liuhua Zhang, Wenbin Zhang, Xin Huang, Vasileios Iosifidis, Zhen Liu, Mingli Zhang, Enza Messina, Ji Zhang
2021 arXiv   pre-print
In designing the system, the discrete wavelet transforms (Daubechies 2, Daubechies 4, and Biorthogonal 6.8) and the Fourier cosine transform were first used to parse the mammogram images and extract statistical  ...  In this work, a computer-aided automatic mammogram analysis system is proposed to process the mammogram images and automatically discriminate them as either normal or cancerous, consisting of three consecutive  ...  S −1 w S b W = λW (13) It can be seen that W is the eigenvector of matrix S −1 w S b . • Linear embedding: with the substitution of eigenvector, W best is easy to find by the following equation: W = S  ... 
arXiv:2012.03151v2 fatcat:lknbprqjjvfgllokfvu563kza4

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

Syed Zohaib Hassan Naqvi, Mohammad Ahmad Choudhry
2020 Sensors  
Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment  ...  A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20226512 pmid:33202613 fatcat:4dooxcpklbeazbwk3itf2nexae

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Safia Abbas, Abeer M Mahmoud
2020 Indonesian Journal of Electrical Engineering and Informatics (IJEEI)  
The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase.  ...  Moreover, using small filters with such type of images ensures better and faster performance with more deep learning.  ...  ACKNOWLEDGEMENTS This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.  ... 
doi:10.52549/ijeei.v8i4.2436 fatcat:x6hdow4gbbeynicdomh2v7oolm

Off-Person ECG Biometrics Using Spatial Representations and Convolutional Neural Networks

Iulian B. Ciocoiu, Nicolae Cleju
2020 IEEE Access  
We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU. The UofT ECG database was provided by the University of Toronto.  ...  ACKNOWLEDGMENT This research has been conducted under a research grant of the Technical University of Iasi, project number TUIASI-GI-2018-0127.  ...  More specifically, we began by using the Maximum Overlap Discrete Wavelet Transform (MODWT), an undecimated wavelet transform operating on arbitrary length sequences [38] ) in order to decompose the raw  ... 
doi:10.1109/access.2020.3042547 fatcat:2rh66mnqonblxpak6nlwndhice

Retinopathy of Prematurity Vessel and Ridge Parameters Measurement by Unsupervised Algorithm

S. Prabakar, K. Porkumaran, Parag K. Shah, V. Narendran
2014 Research Journal of Applied Sciences Engineering and Technology  
The ridge formation of this pathological disorder has also been obtained by the IUWT.  ...  Then an automatic method Isotropic Un-decimated Wavelet Transform (IUWT) has been proposed to extract the abnormal retinal blood vessel and measure its width and tortuosity.  ...  The suggestions and comments of anonymous reviewers, which have greatly helped to improve the quality of this study, are also acknowledged.  ... 
doi:10.19026/rjaset.7.702 fatcat:cli2iwftt5fcjef4ulind6orkq

CardioXNet: A Novel Lightweight CRNN Framework for Classifying Cardiovascular Diseases from Phonocardiogram Recordings [article]

Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil
2020 arXiv   pre-print
The process has been automated by the involvement of two learning phases namely, representation learning and sequence residual learning.  ...  For resolving this issue, in this paper, we introduce CardioXNet,a novel lightweight CRNN architecture for automatic detection of five classes of cardiac auscultation namely normal, aortic stenosis, mitral  ...  Among the feature extraction techniques, fast Fourier transform (FFT), short Fourier transform (STFT), discrete wavelet transform (DWT), continuous wavelet transform (CWT), Q wavelet transform (TQWT),  ... 
arXiv:2010.01392v1 fatcat:7w3mv5mkozadjnbgjbtpsxdmtq
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