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Automated system for chromosome karyotyping to recognize the most common numerical abnormalities using deep learning
2020
IEEE Access
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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. ...
We would like to thank Center of Excellence in Genomic Medicine Research (CEGMR) fro providing us the dataset and the valuable information. ...
ACKNOWLEDGMENT This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (DG-21-611-1441). ...
doi:10.1109/access.2020.3019937
fatcat:rtjhsliqdrbxzbxvpe7h5ympze
Leopard: fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution
[article]
2019
bioRxiv
pre-print
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Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features hundred-fold to thousand-fold speedup compared to current many-to-one machine learning methods. ...
processes and human diseases. ...
Convolutional neural network architecture The architecture of Leopard was adapted from the image segmentation neural network model, U-Net, which generates pixel-wise labels for every pixel in the input ...
doi:10.1101/856823
fatcat:lhuxi4x5xfh5pcipvzjvzbr7gi
Image Segmentation of Zona-Ablated Human Blastocysts
[article]
2020
arXiv
pre-print
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Other Versions
2019
2019 IEEE 13th International Conference on Nano/Molecular Medicine & Engineering (NANOMED)
unpublished
doi:10.1109/nanomed49242.2019.9130621 fatcat:wccuigur4nhppbed6subdcmwoy
doi:10.1109/nanomed49242.2019.9130621 fatcat:wccuigur4nhppbed6subdcmwoy
Automating human preimplantation embryo grading offers the potential for higher success rates with in vitro fertilization (IVF) by providing new quantitative and objective measures of embryo quality. ...
In this work, a deep learning based human blastocyst image segmentation method is presented, with the goal of facilitating the challenging task of segmenting irregularly shaped blastocysts. ...
They used residual units instead of plain neural units to form a more robust architecture for better performance. ...
arXiv:2008.08673v1
fatcat:l27lqlo6t5dztp747qp2no6giy
A New Multiple-Distribution GAN Model to Solve Complexity in End-to-End Chromosome Karyotyping
2020
Complexity
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An end-to-end chromosome karyotype analysis system is proposed over medical big data to automatically and accurately perform chromosome related tasks of detection, segmentation, and classification. ...
Karyotyping refers classifying human chromosomes. ...
Afterwards, sufficient samples generated by MD-GAN are applied to fine-tune pretrained convolutional neural network (CNN) for accurate classification of chromosomes. ese steps are presented in Algorithm ...
doi:10.1155/2020/8923838
fatcat:v27tt63qi5f43gq2drd5soir6m
Adversarial Multiscale Feature Learning Framework for Overlapping Chromosome Segmentation
2022
Entropy
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In this paper, we present an adversarial, multiscale feature learning framework to improve the accuracy and adaptability of overlapping chromosome segmentation. ...
However, the strip-shaped chromosomes easily overlap each other when imaged, significantly affecting the accuracy of the subsequent analysis and hindering the development of chromosome analysis instruments ...
Altinsoy et al. proposed a raw G-band chromosome image segmentation method using convolution network [25] , but it did not work for overlapping chromosomes. ...
doi:10.3390/e24040522
pmid:35455185
pmcid:PMC9029931
fatcat:xmyyzmetvrebnmeqmydglymdr4
Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques
2022
Journal of Healthcare Engineering
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The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. ...
In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. ...
Figure 6 : 6 Figure 6: Basic diagram for the working of CNN.
Figure 7 : 7 Figure 7: e Convolutional Neural Network basic architecture.
Figure 9 :Figure 8 : 98 Figure 9: Preprocessing results. ...
doi:10.1155/2022/2693621
pmid:35047149
pmcid:PMC8763556
fatcat:rzhgpylscjautmfhbl3eno4jqm
Using an evolutionary approach to explore convolutional neural networks for acoustic scene classification
2018
Workshop on Detection and Classification of Acoustic Scenes and Events
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Due to the large number of parameters and the complex inner workings of a neural network, finding a suitable configuration for a respective problem turns out to be a rather complex task for a human. ...
In this paper we, propose an evolutionary approach to automatically generate a suitable neural network architecture and hyperparameters for any given classification problem. ...
Among others the neuro-evolution method, which relies on evolutionary algorithms (EAs), has been a prominent choice for the task of automatically generating neural networks (NNs). ...
dblp:conf/dcase/RoletscheckWSSA18
fatcat:ztvsszn7mjfexe5zriruodvp4e
A Review of Various Methods of Predicting Cervical Cancer
2019
International Journal of Computer Applications
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Due to the complexity of the cell nature, still it is a continuous problem for automating this procedure. ...
This paper discusses Machine Learning algorithms like GLCM (Gray Level Co-occurrence Matrix), SVM (Support Vector Machines), k-NN (k-Nearest Neighbours CNNs (Convolutional Neural Networks), ), MARS (Multivariate ...
Tabrizi, feedforward decision and values boundaries of on Feedforward MLP MLP neural gained from the overlapping cells Neural Network and network ANN model ThinPrep (LMFFNN) 7 Histopathological Cell Image ...
doi:10.5120/ijca2019918596
fatcat:qdvvytf7gbbtlm6p4yrnwya4am
Classification of Cervical Cancer Using Artificial Neural Networks
2016
Procedia Computer Science
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The ANN uses several architectures for easy and accurate detection of cervical cells. ...
Artificial neural network (ANN) plays an important role in many medical imaging applications. ...
A super pixel which uses a convolution neural network (CNN) based segmentation is proposed for more accurate results. The segmentation of cytoplasm is performed first. ...
doi:10.1016/j.procs.2016.06.105
fatcat:h7kpnhqtufhtbave3hhmpq7cmy
Efficient Object Recognition using Convolution Neural Networks Theorem
2017
International Journal of Computer Applications
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We hope to overcome this issue by using Convolution Neural Network (CNN) Theorem. ...
layers of the deep architectures of neurons. ...
INTRODUCTION The concept of convolution neural networks takes inspiration from the human brain. ...
doi:10.5120/ijca2017913123
fatcat:gwxqn5x2e5b3plawwdvgzlgrey
Deep learning-based quantification of abdominal fat on magnetic resonance images
2018
PLoS ONE
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of well-known neural networks. ...
A deep learning approach was developed for segmenting visceral and subcutaneous fat based on the U-net architecture made publicly available through the open-source ANTsRNet library-a growing repository ...
U-net for segmentation of abdominal fat in MRI U-net is a well-known convolutional neural network architecture for voxelwise classification labeling [https://arxiv.org/abs/1505.04597]. ...
doi:10.1371/journal.pone.0204071
pmid:30235253
pmcid:PMC6147491
fatcat:eezvji4jbjguzib3g4sisr6q5a
Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images
2021
Applied Sciences
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The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer ...
Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. ...
Sanchez [55] also uses a deep convolutional neural network for malaria parasite detection. Pan et al. [52] proposed a deep convolutional neural-network-based algorithm for malaria detection. ...
doi:10.3390/app11052284
fatcat:w5utaierwnbjfjmcdsvtsb276a
Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images
[article]
2020
arXiv
pre-print
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In this context, we present a complete pipeline for the segmentation of nuclei in Feulgen-stained images using Convolutional Neural Networks. ...
It is usually done by cytological exams which consist of visually inspecting the nuclei searching for morphological alteration. Since it is done by humans, naturally, some subjectivity is introduced. ...
Adriane Pogere, for providing the samples and Ms. Ane Francyne Costa for all necessary assistance for data collection. ...
arXiv:2002.08331v1
fatcat:6ue4uuaowfggnpa2gs3tm6tl3e
Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives
2022
Engineered Science
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Dedicated image analysis algorithms provide mathematical description of the region of interest which provide a great support to pathologists for decision making. ...
The study accentuates the future directions pertinent to the development of cost-effective, automated disease classification system that should be a significant advantage for countries with limited resources ...
Ranjini Kudva, Professor, and all the staff in the Dept. of Pathology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India for providing all support and guidance for ...
doi:10.30919/es8d633
fatcat:j2kjggmqjrbz3dvy25faw2zki4
Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images
2020
Anais do Computer on the Beach
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In this context,we present a complete pipeline for the segmentation of nucleiin Feulgen-stained images using Convolutional Neural Networks.Here we show the entire process of segmentation, since the collectionof ...
Since itis done by humans, naturally, some subjectivity is introduced. Computationalmethods could be used to reduce this, where the firststage of the process would be the nuclei segmentation. ...
Adriane Pogere, for providing the samples and Ms. Ane Francyne Costa for all necessary assistance for data collection. ...
doi:10.14210/cotb.v11n1.p169-175
fatcat:nck4ck3bpzajjcl64cnapqr2fm
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