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An effective tumor classification with deep forest and self-training

Zhanbo Chen, Zhanbo Chen, Xiaojun Sun, Lili Shen
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
We wish training style that samples can be implemented to train by from high-to low-confidence, self-training can meet this requirement, and the deep forest approach with the hyper-parameter settings used  ...  Therefore, in this paper, we present a novel semi-supervised learning approach with a deep forest model to increase the performance of tumor classification, which employs unlabelled samples and minimizes  ...  The proposed method by deep forest and semi-supervised learning with self-training (DFST), can be implemented in disease diagnosis decision-making in this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3096241">doi:10.1109/access.2021.3096241</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/imot75ahnjglxmef6mbua37s6i">fatcat:imot75ahnjglxmef6mbua37s6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210714040027/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09481164.pdf?tp=&amp;arnumber=9481164&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e6/51/e65116690712180f0571cf27702a9d274cd2121b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3096241"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Chunwu Yin, Zhanbo Chen
<span title="2020-08-24">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7happfjl2zh5fbym2owb7p7kni" style="color: black;">Healthcare</a> </i> &nbsp;
Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled  ...  The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance.  ...  With deep forest as a base model, semi-supervised learning such as self-training provides more high-confidence labelled samples for deep forest training.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare8030291">doi:10.3390/healthcare8030291</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32846941">pmid:32846941</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5bz4hwc2gbfw7jnrdjoli33jai">fatcat:5bz4hwc2gbfw7jnrdjoli33jai</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200902024650/https://res.mdpi.com/d_attachment/healthcare/healthcare-08-00291/article_deploy/healthcare-08-00291-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/04/d4/04d4c6090b73c936a6458ccbb2a442071808a943.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare8030291"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma [article]

Liangrui Pan, Hetian Wang, Lian Wang, Boya Ji, Mingting Liu, Mitchai Chongcheawchamnan, Jin Yuan, Shaoliang Peng
<span title="2022-04-29">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The experimental results demonstrate that our method outperforms the traditional and deep learning approaches on various evaluation metrics, with an accuracy of 99.17% to support osteosarcoma diagnosis  ...  The purpose of this study is to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis, which will help doctors reduce their workload, improve  ...  Adam is the optimizer of the deep learning models. All models were trained with an NVIDIA V100. C.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.13838v1">arXiv:2204.13838v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/endcg33civbm5cvj76hu2ojp6u">fatcat:endcg33civbm5cvj76hu2ojp6u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220502191402/https://arxiv.org/ftp/arxiv/papers/2204/2204.13838.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/15/35/1535297db20862ed1c416f2352426c6e3e3c53f4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.13838v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
<span title="2020-02-22">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5hwrtdnkjvclroyxzt4ty5ijb4" style="color: black;">Brain Sciences</a> </i> &nbsp;
Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer  ...  Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification  ...  A kernel-based CNN combined with M-SVM presents an effective method for the enhancement and automatic segmentation of tumors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/brainsci10020118">doi:10.3390/brainsci10020118</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32098333">pmid:32098333</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7071415/">pmcid:PMC7071415</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wofq4puvcbemlconbz6carsf2y">fatcat:wofq4puvcbemlconbz6carsf2y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200226222818/https://res.mdpi.com/d_attachment/brainsci/brainsci-10-00118/article_deploy/brainsci-10-00118.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9d/41/9d411d749cdc684d003c870f061acc4109c60a7a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/brainsci10020118"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071415" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

On the Methods for Detecting Brain Tumor from MRI images

<span title="2020-08-11">2020</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cj3bm7tgcffurfop7xzswxuks4" style="color: black;">VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE</a> </i> &nbsp;
The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning.  ...  Here we present a review of few methods from simple thresholding to advanced deep learning methods for segmentation of tumor from MRI data.  ...  A Self Organizing Map (SOM) is type of ANN in which training is done in an unsupervised way to reduce the dimension of the training samples called a map.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.i1007.0799s20">doi:10.35940/ijitee.i1007.0799s20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mpo2gpnwwvhv3fj74idihf4uom">fatcat:mpo2gpnwwvhv3fj74idihf4uom</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220223135440/https://www.ijitee.org/wp-content/uploads/papers/v9i9s/I10070799S20.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b1/50/b1502a33da4124e6538248ed443ed57e1239839f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.i1007.0799s20"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

MRI Brain Tumor Detection Using Boosted Crossbred Random Forests and Chimp Optimization Algorithm Based Convolutional Neural Networks

<span title="2022-04-30">2022</span> <i title="The Intelligent Networks and Systems Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hxhhpqr6nrfs5eblsflinrd2wa" style="color: black;">International Journal of Intelligent Engineering and Systems</a> </i> &nbsp;
In this paper, an effective tumor detection framework is proposed by utilizing a hybrid segmentation algorithm and a new hyper-parameter optimized Convolutional Neural Networks (CNN) based classifier using  ...  No tumor, Glioma, Meningioma, and Pituitary tumor are classes of the MRI images which are correctly classified by the proposed BCRF and COA-CNN based detection model with a testing accuracy of 95.18% and  ...  [21] utilized the deep neural network with generative adversarial networks (GAN) pre-training to improve the tumor type classification accuracy.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22266/ijies2022.0430.04">doi:10.22266/ijies2022.0430.04</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vxx6fhqpgvb3lptjyrsplfrxhu">fatcat:vxx6fhqpgvb3lptjyrsplfrxhu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220427202747/https://inass.org/wp-content/uploads/2021/08/2022043004-2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1a/eb/1aeb3824d4700106841760b86cd2069d6caa1c2b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22266/ijies2022.0430.04"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Breast cancer classification using machine learning techniques: a comparative study

Djihane HOUFANI, LINFI Laboratory, University of Biskra, Algeria, Sihem SLATNIA, Okba KAZAR, Noureddine ZERHOUNI, Hamza SAOULI, Ikram REMADNA
<span title="2020-11-02">2020</span> <i title="Knowledge Kingdom Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sautypmqzzf3dejaszykiquxmy" style="color: black;">Medical Technologies Journal</a> </i> &nbsp;
We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification.  ...  Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave the best results with an accuracy of 98% for breast cancer classification  ...  Forest model with GA-based 14 features The system gave the best results (accuracy 99.48%).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.26415/2572-004x-vol4iss2p535-544">doi:10.26415/2572-004x-vol4iss2p535-544</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3b43ns3c4zfohpylxtmcrkyf7e">fatcat:3b43ns3c4zfohpylxtmcrkyf7e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107041750/http://medtech.ichsmt.org/index.php/MTJ/article/download/248/178" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/86/be/86be66a083ec86196830d655c2c46790fc187a26.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.26415/2572-004x-vol4iss2p535-544"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Gene Transformer: Transformers for the Gene Expression-based Classification of Lung Cancer Subtypes [article]

Anwar Khan, Boreom Lee
<span title="2021-10-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Hence, we propose an end-to-end deep learning approach, Gene Transformer, which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant  ...  The classification results show that Gene Transformer can be an efficient approach for classifying cancer subtypes, indicating that any improvement in deep learning models in computational biology can  ...  for tumor classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.11833v3">arXiv:2108.11833v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/we55g22pkvfvtiqhsi7tmvx6yq">fatcat:we55g22pkvfvtiqhsi7tmvx6yq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211029051334/https://arxiv.org/ftp/arxiv/papers/2108/2108.11833.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6e/47/6e47ccb8f508fc83c5dcfcdd79cf865594593bd9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.11833v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Aina Umairah Mazlan, Noor Azida Sahabudin, Muhammad Akmal Remli, Nor Syahidatul Nadiah Ismail, Mohd Saberi Mohamad, Hui Wen Nies, Nor Bakiah Abd Warif
<span title="2021-08-22">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vt2hc3xcijfofb7cwnxv4hhszi" style="color: black;">Processes</a> </i> &nbsp;
This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology.  ...  Data-driven model with predictive ability are important to be used in medical and healthcare.  ...  A trained SVM with different kernels as KSVM was used for the brain tumor classification. The results showed that the proposed method was effective and fast.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr9081466">doi:10.3390/pr9081466</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mehvzadmn5dafauq4lqifb2vky">fatcat:mehvzadmn5dafauq4lqifb2vky</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210901093308/https://res.mdpi.com/d_attachment/processes/processes-09-01466/article_deploy/processes-09-01466-v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c2/e7/c2e733f97e4650ae2ba4c695ca0a44f9f717dec1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/pr9081466"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Machine Learning-Based Gynecologic Tumor Diagnosis and Its Postoperative Incisional Infection Influence Factor Analysis

Qian Shen, Ling Wang, Rahim Khan
<span title="2021-11-23">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cswd2rqrire6lgrsm56kv4adue" style="color: black;">Journal of Healthcare Engineering</a> </i> &nbsp;
tumors.  ...  In this study, 74 surgically treated gynecologic tumor patients were randomly selected from within the hospital as the study population and were divided into study and control groups.  ...  Collection and assembly of data were performed by all authors. Data analysis and interpretation were performed by all authors. Manuscript writing was carried out by all authors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/7956184">doi:10.1155/2021/7956184</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34858564">pmid:34858564</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8632385/">pmcid:PMC8632385</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jaumepndwrcallamppt2zow6ja">fatcat:jaumepndwrcallamppt2zow6ja</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220206115406/https://downloads.hindawi.com/journals/jhe/2021/7956184.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bb/ee/bbee8d3a3517c2a9c4b17a5413724f38b9a357d9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/7956184"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632385" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
<span title="2019-02-27">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wdwg5aetkjbgpga7kn2jevifmi" style="color: black;">Computers in Biology and Medicine</a> </i> &nbsp;
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years.  ...  imaging and positron emission tomography imaging.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.compbiomed.2019.02.017">doi:10.1016/j.compbiomed.2019.02.017</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31054502">pmid:31054502</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6531364/">pmcid:PMC6531364</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tcyorm6g3ff6dg7ty2ubtqorjq">fatcat:tcyorm6g3ff6dg7ty2ubtqorjq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826220658/https://arxiv.org/pdf/1903.11726v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/64/16/6416f74798aaedb1ea27321a1fd9f42be71a7ac4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.compbiomed.2019.02.017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6531364" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

iTAGPred: A Two-Level Prediction Model for Identification of Angiogenesis and Tumor Angiogenesis Biomarkers

Khalid Allehaibi, Yaser Daanial Khan, Sher Afzal Khan, Jose Merodio
<span title="2021-09-27">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sgcj6txpjbc7djdaoaokofbzzy" style="color: black;">Applied Bionics and Biomechanics</a> </i> &nbsp;
In the case the protein is an angiogenesis protein, then the second level predicts whether the protein is linked with tumor angiogenesis or not.  ...  The overall accuracy using an RF classifier for angiogenesis at the first level was 97.8% and for tumor angiogenesis at the second level was 99.5%, ANN showed 94.1% accuracy for angiogenesis and 79.9%  ...  The authors, therefore, acknowledge with thanks DSR technical and financial support.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2803147">doi:10.1155/2021/2803147</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34616486">pmid:34616486</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8490072/">pmcid:PMC8490072</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fuqhofeqtbdljkrbmrnmcufgoy">fatcat:fuqhofeqtbdljkrbmrnmcufgoy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211003221928/https://downloads.hindawi.com/journals/abb/2021/2803147.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bc/33/bc335363b7d223dd653482eec8a9586ed63d492d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2803147"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490072" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak
<span title="2021-03-22">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Brain tumor classification plays an important role in clinical diagnosis and effective treatment.  ...  To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we  ...  Acknowledgments: The authors would like to thank the editors and all the reviewers for their valuable comments on this manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21062222">doi:10.3390/s21062222</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33810176">pmid:33810176</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wbeoxfqfr5d2xhdfgzlraey2ge">fatcat:wbeoxfqfr5d2xhdfgzlraey2ge</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210408070533/https://res.mdpi.com/d_attachment/sensors/sensors-21-02222/article_deploy/sensors-21-02222-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4d/8f/4d8f0ae904779a50b2e18fec49e51a5661a98d8a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21062222"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review [article]

Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
<span title="2020-09-30">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images.  ...  The main findings in the classification process reveal that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction.  ...  Acknwoledgments: VL would like to acknowledge support by a Discovery grant from the Natural Sciences and Engineering Research Council of Canada.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.00961v1">arXiv:2010.00961v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mrzh7mdlifduziuxqpokovueee">fatcat:mrzh7mdlifduziuxqpokovueee</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201006010101/https://arxiv.org/ftp/arxiv/papers/2010/2010.00961.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.00961v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification

Sreenivas Eeshwaroju, Harman Connected Services, Novi, Michigan, USA, Praveena Jakula, Intel Corporation, USA.
<span title="">2020</span> <i title="MNAA Pub World"> Journal of Computational Science and Intelligent Technologies </i> &nbsp;
Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN).  ...  MRI images are especially used in this research to diagnose tumor within the brain with classification results.  ...  The self-organizing neural mapping network initiates [7] by training the features extracted from the discrete wavelet mixing wavelets and thus train the K-nearest neighbor in the filter factors and complete  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.53409/mnaa.jcsit20201305">doi:10.53409/mnaa.jcsit20201305</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dopnuzor7jhcnneklqv5xirnim">fatcat:dopnuzor7jhcnneklqv5xirnim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210624082530/https://mnaapub.com/JCSIT/2020/Volume_1/Issue_3/paper_5_vol.1_iss.3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cd/10/cd1083939930ec72cd03bd1357cd5b91127d29b7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.53409/mnaa.jcsit20201305"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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