Cargando…

Cancer Diagnosis Using Deep Learning: A Bibliographic Review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Bord...

Descripción completa

Detalles Bibliográficos
Autores principales: Munir, Khushboo, Elahi, Hassan, Ayub, Afsheen, Frezza, Fabrizio, Rizzi, Antonello
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770116/
https://www.ncbi.nlm.nih.gov/pubmed/31450799
http://dx.doi.org/10.3390/cancers11091235
_version_ 1783455396104503296
author Munir, Khushboo
Elahi, Hassan
Ayub, Afsheen
Frezza, Fabrizio
Rizzi, Antonello
author_facet Munir, Khushboo
Elahi, Hassan
Ayub, Afsheen
Frezza, Fabrizio
Rizzi, Antonello
author_sort Munir, Khushboo
collection PubMed
description In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.
format Online
Article
Text
id pubmed-6770116
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-67701162019-10-30 Cancer Diagnosis Using Deep Learning: A Bibliographic Review Munir, Khushboo Elahi, Hassan Ayub, Afsheen Frezza, Fabrizio Rizzi, Antonello Cancers (Basel) Review In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements. MDPI 2019-08-23 /pmc/articles/PMC6770116/ /pubmed/31450799 http://dx.doi.org/10.3390/cancers11091235 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Munir, Khushboo
Elahi, Hassan
Ayub, Afsheen
Frezza, Fabrizio
Rizzi, Antonello
Cancer Diagnosis Using Deep Learning: A Bibliographic Review
title Cancer Diagnosis Using Deep Learning: A Bibliographic Review
title_full Cancer Diagnosis Using Deep Learning: A Bibliographic Review
title_fullStr Cancer Diagnosis Using Deep Learning: A Bibliographic Review
title_full_unstemmed Cancer Diagnosis Using Deep Learning: A Bibliographic Review
title_short Cancer Diagnosis Using Deep Learning: A Bibliographic Review
title_sort cancer diagnosis using deep learning: a bibliographic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6770116/
https://www.ncbi.nlm.nih.gov/pubmed/31450799
http://dx.doi.org/10.3390/cancers11091235
work_keys_str_mv AT munirkhushboo cancerdiagnosisusingdeeplearningabibliographicreview
AT elahihassan cancerdiagnosisusingdeeplearningabibliographicreview
AT ayubafsheen cancerdiagnosisusingdeeplearningabibliographicreview
AT frezzafabrizio cancerdiagnosisusingdeeplearningabibliographicreview
AT rizziantonello cancerdiagnosisusingdeeplearningabibliographicreview