Table Of ContentStudies in Computational Intelligence 908
Utku Kose
Jafar Alzubi Editors
Deep Learning
for Cancer
Diagnosis
Studies in Computational Intelligence
Volume 908
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
The books of this series are submitted to indexing to Web of Science,
EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.
More information about this series at http://www.springer.com/series/7092
Utku Kose Jafar Alzubi
(cid:129)
Editors
Deep Learning for Cancer
Diagnosis
123
Editors
UtkuKose Jafar Alzubi
Department ofComputer Engineering Faculty of Engineering
Süleyman Demirel University Al-Balqa AppliedUniversity
Isparta, Turkey Balqa, Jordan
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
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Foreword by Dr. Omer Deperlioglu
Asthefieldofartificialintelligenceisaffectingallfieldsofthemodernlife,theneed
for processing big amount of data has been solved effectively with deep learning
techniques. With the start of the twenty-first century, computational technologies
have started to gain a momentum to solve time complexity appeared while using
traditional techniques of machine learning/artificial intelligence. At this point, the
deep learning has become a strong actor in contributing to the unstoppable rise of
intelligent systems. As the future of artificial intelligence has many more surprises
for all of us, current using alternatives of deep learning takes researchers’ interest
too much. Among all its employments within different fields, maybe the field of
medicalisoneofthemostvitalplatformswherethefutureofhumankindisshaped
rapidly.
Inthisbook,titledasDeepLearningforCancerDiagnosis,youwillfindlotsof
recent works focusing on the essentials of deep learning, its application types for
cancerdiagnosis,andevengeneraloverviewofhowthefutureofdeeplearningwill
changeandimproveinthenexttimeperiod.Ascancerisoneofthemostimportant
diseases threatening the existence of humankind, it has been a very long time
medical and many supportive fields are working hardly for developing effective
solutions. Except from medicine works, which are generally focused on the treat-
ment process of cancer, it is too important to design efficient methodologies for
early diagnosis of different cancer types. As each different type of cancer is
affecting different parts and organs of our body, it has been always too difficult to
get something like a multi-cancer diagnosis system. But in my opinion, it is now
easier—as not done like before—to run computationally effective and accurate
automatedsystemsthankstoartificialintelligenceandthesubfieldofdeeplearning.
Youwillseeinthisbookthatdeeplearningcanbeasingleorhybridsolution(with
especially image processing) for effective detection of tumors and diagnosis of
different cancer types. As deep learning is really a deep subfield with many com-
binations ofdeeperneuralnetworktypes,youwillseethatthereisagreateffortin
designing unique, alternative models to diagnose different cancer types such as
braintumors/gliomas,breastcancer,lungcancer,melanoma,andfibrosarcoma.Itis
remarkable that each of the chapter included in this book uses pure language to
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vi ForewordbyDr.OmerDeperlioglu
explainalltechnicaldetailstoawideaudienceofbeginnerstoadvancedresearchers
enrolled in the context of artificial intelligence, cancer diagnosis, and biomedical-
based topics. I suggest all readers to consider the following points: (1) Starting
sections of each chapter explain the essentials of deep learning approaches,
methods, and techniques from different perspective. So, you may read the whole
book firstly to understand the essentials of deep learning. (2) After that, you can
read further sections to get adapted to the employment of different techniques for
diagnosis processes. Except from these, it is also easy to get informed about how
specific types of cancers are diagnosed thanks to deep learning-based methods, by
reading the associated chapters separately. After finishing that book, you will be
ready to work on cancer diagnosis via deep learning and/or improve your knowl-
edgeskillsforfurtherresearch,inwhichtheliteraturesofartificialintelligence and
medical are both still needing. I suggest all the colleagues that they can use that
work in their lectures about especially artificial intelligence and its use within
medical–biomedicalproblems.Inthiscontext,thebookopensaviewwindowfora
great collaboration of computer science and the medical, by considering even a
specific but remarkable enough research topic.
I would like to thank all authors for their valuable contributions to create that
book as a great reference for studies on cancer diagnosis and effective use of deep
learning for all phases of that. Just turn the pages to make a start to your journey!
Dr. Omer Deperlioglu
Afyon Kocatepe University
Afyonkarahisar, Turkey
e-mail: [email protected]
Foreword by Dr. Jose Antonio
Marmolejo-Saucedo
Machinelearningisknownasthelocomotiveoftheartificialintelligenceasitisthe
key factor of building autonomous systems. After artificial intelligence started to
take an active part in especially daily life, the future view for the world in science
fiction movies has been started to be discussed widely. Here, more active role in
daily life caused machine learning solutions to be included in many devices and
softwaresystemsweareusing.Bytheway,thecapabilitiesofintelligentsystemsto
learn from samples and even learning something in an evolutionary way caused
researchers in all fields to transform their works into innovative forms including
running of intelligent solution mechanisms on the background. As the automation
has been already transforming in a technological manner, the age of Industry 4.0
has become the main title of innovations appeared over shoulders of especially
machine learning.
It is remarkable that machine learning is actually an optimization process of
artificialintelligence. Ingeneral,machinelearningtechniquesemployoptimization
processes, which are called as training or learning (Because of that, especially
intelligent optimization algorithms can be used directly for training machine
learning and designing hybrid systems in this way). Since that optimization has
been important as the mathematical background of machine learning, big data
caused researchers to think about designing something new. Called as deep
learning, more complicated versions of well-known artificial neural networks are
currentlyamongthemosteffectivelyusedtechniquesforsolvingtoday’sadvanced
problems. In other terms, the advanced optimization, which is required by bigger
problems, is solved by deep learning nowadays.
Itisapleasureformetowriteaforewordforaworkofusingdeeplearningand
explainingitsactiveroleinthefieldofmedical.TitledasDeepLearningforCancer
Diagnosis, this book is a timely support for the literature of medical as we are
currently experiencing a terrible pandemic by COVID-19 and that is an important
signthatweneedmoreactiveuseoftechnologyandartificialintelligenceforearly
predictionsanddiagnosisofdiseasestreatingourlife.Asweallknow,canceristhe
enemy of the living organisms’ cells and it causes deaths at the end if it is not
treated effectively. Currently, there are different vital types of cancers, and they all
vii
viii ForewordbyDr.JoseAntonioMarmolejo-Saucedo
needasearlyaspossiblediagnosisforsuccessfultreatmentattheend.Inthissense,
deeplearningtechniqueshavebeen accurately usedfor diagnosing cancer, andthe
field of medical is highly interested in effective collaborations for automated sys-
tems for ensuring high-level solutions that can diagnose and treat better than
humans. In this book, readers will be able to read about how deep learning is
appliedforthediagnosisprocess,asincludingpre-workandpost-workapproaches
changing according tothe medicaldata considered. The associated literature istoo
activeinmedicaldiagnosisbyartificialintelligence,anditseemsthatthefuturewill
be more enrolled in early diagnosing, worldwide solutions to ensure a sustainable,
disease-free life for humankind.
The chapters included in the book target especially important types of cancers,
and according to me, all these chapters have key points, which can enable the
readers to get necessary ‘know-how’ about applying deep learning in medical
diagnosis. In this way, the readers will get informed about using artificial intelli-
gence for not only cancer but also for different diseases, which are caused by
viruses, genes, and even harmful environmental factors resulting in evolutionary
changes leading to new types of diseases. Hopefully, the science has always
solutions and that book will train open minds for better solutions for the future.
Dr. Jose Antonio Marmolejo-Saucedo
Universidad Panamericana
Mexico City, Mexico
e-mail: [email protected]
Preface
The field of artificial intelligence has a very wide of diversity in terms of different
computational and logical solutions for real-world problems. As long as we can
conducttheexactmodelingofareal-worldproblem,itisalwayspossibletorunan
effective artificial intelligence algorithm technique for successful results. The need
fortheartificialintelligenceisgenerallyassociatedwithmoreaccurateandefficient
results for problems, which humankind cannot solve with high success rates or
provide an exact solution yet. So, artificial intelligence has been a great tool for
technologicaldevelopmentsandthathasbeenfeelingtoomuchbythecommunity,
especiallyin2000s.Aslikemanyothertechnologiesareevolvingintime,thatfield
ofintelligentsystemshasbeenevolvingtoo,andasaresult,wehavedifferentkinds
ofsolutionsintroducedtotheliterature.Recently,thereisastormofdeeplearning,
which is a collection—at least for now—of advanced neural network models with
specificlayer types that enable ustodeal withcomplex, bigamount,andreal-time
data.Becausetheresultsbydeeplearningaretoosuccessful,ithasbeenemployed
within all fields of modern life rapidly. The field of medical is among them.
While we are writing that preface, humankind is experiencing a fatal pandemic
causedbyCOVID-19typevirus,andthereisagreatemergencystatewithinevery
country as including breaks in educational institutions, curfews, and valuable
medicaleffortstofinddiagnosisandtreatmentsolutionsagainstthatvirus.Here,the
role of technology is too remarkable that there is a race against time as deaths
caused by COVID-19 are increasing fast if there is no immediate action taken.
Movingfromthatfact,itispossibletoindicatethatthefutureofhumankindwillbe
associatedwithimportantdevelopmentsforespeciallywell-being,ensuringastable
health state within all over the world, and keeping sustainability oriented green
solutions to keep the balance of the life at a certain level. That includes research
efforts for the active run of technology within problems of medical and even its
associated fields such as biology, genetics, and chemistry.
In this context, we would like to introduce our edited work: Deep Learning for
Cancer Diagnosis, as the latest collections of a total of 16 valuable chapters tar-
geting different aspects of how deep learning is applied for deriving successful
solutions for another vital enemy of humankind: cancer. According to the latest
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