Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.
TLDR
The study highlights how cancer diagnosis, cure process is assisted using machine learning with supervised, unsupervised and deep learning techniques.About:
This article is published in Journal of Infection and Public Health.The article was published on 2020-09-01 and is currently open access. It has received 135 citations till now.read more
Citations
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Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture
TL;DR: A new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification and a comparison with existing techniques shows the proposed design yields comparable accuracy.
Journal ArticleDOI
Brain tumor detection and multi-classification using advanced deep learning techniques
TL;DR: In this article, the authors presented segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU).
Journal ArticleDOI
Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification.
TL;DR: In this paper, a deep learning approach was proposed to classify brain tumors using an MRI data analysis to assist practitioners, which comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (ie, 19 layered Visual Geometric Group) model Moreover, the synthetic data augmentation concept was introduced to increase available data size for classifier training.
Journal ArticleDOI
Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons
TL;DR: Wang et al. as mentioned in this paper presented deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide, and their research structure built on a recent analysis of the COVID19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions.
Journal ArticleDOI
Machine learning towards intelligent systems: applications, challenges, and opportunities
TL;DR: In this paper, the authors present some of the challenges facing education, healthcare, network security, banking and finance, and social media, and suggest several research opportunities that benefit from the use of ML to address these challenges.
References
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Journal ArticleDOI
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Freddie Bray,Jacques Ferlay,Isabelle Soerjomataram,Rebecca L. Siegel,Lindsey A. Torre,Ahmedin Jemal +5 more
TL;DR: A status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Journal ArticleDOI
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen,Joost J. M. van Griethuysen,Joost J. M. van Griethuysen,Andriy Fedorov,Chintan Parmar,Ahmed Hosny,Nicole Aucoin,Vivek Narayan,Regina G. H. Beets-Tan,Regina G. H. Beets-Tan,Jean-Christophe Fillion-Robin,Steve Pieper,Hugo J.W.L. Aerts +12 more
TL;DR: PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images, is developed and its application in characterizing lung lesions is demonstrated.