Training phase results.
\r\n\tIn fact, the engineering covers the design, construction, and maintenance of vessels and their associated equipment. Moreover, it also allows the performance by the naval engineer. So, it designs the structure, engines and other components of the ships. In this regard, it must consider the use given to the vessel, the amount of passengers cargo to be transported, the distance to be traveled as well as the place of operation - i.e. rivers, lakes, seas or oceans. On the other hand, the construction is supervised by the technicians and the workers who will verify the raw material quality and the work methodologies accompanied by manufacturers. With these aspects in mind there is also a third one and that is managing the sea and inland waterway transport by controlling the vessel traffic and communication services. To summarize, all these topics are of great importance to naval engineering and are welcome as chapters for this book.
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Centa, Caroline Roques, Maguette Ba, Fatima Medjahed-Hamidi, Frederic Chaubet, Didier Letourneur, Emmanuel Lansac and Anne Meddahi-Pelle",authors:[{id:"46019",title:"Dr.",name:"Anne",middleName:null,surname:"Meddahi-Pellé",fullName:"Anne Meddahi-Pellé",slug:"anne-meddahi-pelle"},{id:"55966",title:"Dr.",name:"Emmanuel",middleName:null,surname:"Lansac",fullName:"Emmanuel Lansac",slug:"emmanuel-lansac"},{id:"55967",title:"Dr.",name:"Isabelle",middleName:null,surname:"Di Centa",fullName:"Isabelle Di Centa",slug:"isabelle-di-centa"},{id:"55968",title:"Dr.",name:"Maguette",middleName:null,surname:"Ba",fullName:"Maguette Ba",slug:"maguette-ba"},{id:"56020",title:"Dr.",name:"Nizar",middleName:null,surname:"Khelil",fullName:"Nizar Khelil",slug:"nizar-khelil"},{id:"61681",title:"Mr.",name:"Rémi",middleName:null,surname:"Escande",fullName:"Rémi Escande",slug:"remi-escande"},{id:"61682",title:"MSc.",name:"Fatima",middleName:null,surname:"Medjahed",fullName:"Fatima Medjahed",slug:"fatima-medjahed"},{id:"61683",title:"Prof.",name:"Frederic",middleName:null,surname:"Chaubet",fullName:"Frederic Chaubet",slug:"frederic-chaubet"},{id:"61684",title:"Dr.",name:"Didier",middleName:null,surname:"Letourneur",fullName:"Didier Letourneur",slug:"didier-letourneur"},{id:"118764",title:"Ms.",name:"Caroline",middleName:null,surname:"Roques",fullName:"Caroline Roques",slug:"caroline-roques"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"76058",title:"Ultrasonic Detection of Down Syndrome Using Multiscale Quantiser with Convolutional Neural Network",doi:"10.5772/intechopen.96502",slug:"ultrasonic-detection-of-down-syndrome-using-multiscale-quantiser-with-convolutional-neural-network",body:'\nDown syndrome (DS) is the chromosomal abnormality caused in humans when extra genes from chromosome 21 are transferred to a newly produced embryo. It affects the fetal development leading to physical and mental abnormalities. The babies with DS have a distinct appearance than normal babies. Some of the DS victims are shown in Figure 1. The babies suffering from DS are likely to have retardation in growth and the mental problems. DS, in general, cannot be cured; the simplest way to avoid the babies with DS is to detect the fetus with DS and prevent it from being born.
\nVictims of DS (courtesy:
DS is not a rare phenomenon and the occurrence of this phenomenon is eventually improving throughout the world. Patterson [1] in his work “Molecular genetic analysis of Down Syndrome” states that more than 1 in 1000 neonates has DS. A stats report [2] shows that i) the risk of DS in the global birth rate is 0.00125% ii) women in the age of 30 and below have 0.001% risk of DS iii) women in the age of 45 have 0.022% risk of DS. The abortion rates in DS-affected pregnancies [3] have amplified to 67–92% in the United States and Europe.
\nIn medical world, ultrasonographic markers like nuchal fold, nasal bone hypoplasia and femur length are often seen as a symptom of DS. Nuchal fold is a skin fold noted at the backside of neck in the fetus during the second trimester. Increased thickness in the nuchal fold is observed as the most sensitive symptom of DS. A comparison of the nuchal fold between the normal fetus and DS affected fetus is shown in Figure 2.
\nComparison of nuchal fold between normal fetus and DS related fetus (courtesy:
Nasal bone hypoplasia is a condition where the nasal bone of the fetus appears to be very small. It has to be noted that 70% of DS [4] fetuses have no nasal bone or smaller nasal bone. A comparison of nasal bone analysis between the normal fetus and DS related fetus is shown in Figure 3.
\nComparison of nasal bone between normal fetus and DS related fetus (courtesy:
Femur length is the measure of the longest bone in the fetus. Shortening of femur length is considered as the symptom of DS. Recently, Echogenic Intracardiac Foci (EIF) had been considered as a new symptom of DS. Many clinical researches affirm that the presence of EIF is considered as a potential indication of DS. An EIF is named as an intense white shape that is found in the heart of the fetus. It can be examined through the ultrasound. An article “Ultrasound Findings” expresses that EIF is seen in around 1 out of each 20 or 30 pregnancies (∼3–5%). EIF is associated with 12% of foetuses with DS [5] and biventricular EIF suffers higher risk for aneuploidy. If an EIF is detected in an ultrasound image, the pregnant women with EIF fetus feel dreadful mental pressure fearing that they would deliver DS affected baby [2]. This psychological weight caused in the pregnant ladies can undesirably affect the fetus. To cut the down the psychological weight of the pregnant ladies and to guarantee that the fetus is unaffected with DS, women with EIF symptoms are prescribed to go through chorionic villus sampling and amniocentesis.
\nAmniocentesis, Percutaneous umbilical blood inspecting and Chorionic villus sampling methods are the most non-ultrasonic method for detecting DS. Amniocentesis is a method of collecting a little amount of the amniotic fluid that surrounds the fetus and analysing it for trisomy 21. It is carried in the light of ultrasonic guidance. It is advised after the 15th week of pregnancy. This method has the high risk of leaking amniotic fluid, miscarriage, needle injury to fetus and infection transmission. Percutaneous umbilical blood sampling (PUBS) is a method of collecting the blood from the umbilical cord and tests it for chromosomal defects. It is performed in the 18th week of the pregnancy. PUBS have a higher risk of miscarriage. Chorionic villus sampling (CVS) is a method of analysing the chromosomes in the cells taken from the placenta. CVS is performed between 9th and 14th weeks of the pregnancy. This method suffers from the drawbacks like miscarriage and uterine infection problems.
\nAs the relativity of EIF with DS is very low, it would not be advisable to request women with EIF fetus to undergo Amniocentesis, CVS and PUBS. There is a great need to coin a new mechanism that can differentiate DS related EIF fetus from the normal EIF fetus through ultrasound testing. This research aims at bringing a new invention that can detect DS based on EIF through an ultrasound scan.
\nThe major contributions of this chapter are:
A new medical parameter EIF is used for the detection of DS
A new image segmentation algorithm Multi-scale Quantized Convolution Neural Network (MSQCNN) is developed and used for accurately detecting and extracting the EIF in the ultrasound fetal images
Cross-Correlation Technique (CCT) is employed to confirm DS by analysing the nasal bone hypoplasia in the training phase
A new supervised classification scheme Enhanced Learning Vector Quantiser (ELVQ) is utilised to differentiate the normal EIF from the EIF related to DS.
Introduction of the problem statement, the necessity for this research and the challenges present in this research had already been well discussed in section 1. The rest of this chapter is structured as follows. A complete view of related studies is provided in section 2. The proposed methodology is elucidated in section 3. The test results and comparisons are elaborated in section 4. Future research directions and conclusion are briefed in section 5 and section 6.
\nThis section provides a detailed view of the emerging researches performed on the identification of DS utilising ultrasound. Rebecca et.al [6] performed analyses to decide the exactness of second-trimester ultrasound in identifying DS in fetuses. The malformation in the structure and ultrasonographic markers are the baseline factor behind this research examination. The experimental outcomes demonstrated that the ultrasonographic markers without related morphological abnormalities could not differentiate between the normal fetus and the fetus with DS. This research made a noteworthy observation that placing the markers as a deciding factor to suggest amniocentesis can lead to a huge number of fetal losses.
\nLauren Lynch et.al [7] brought forth the new ultrasonic parameter nuchal fold that was highly informative for detection of DS than the other parameters like biparietal diameter and femur length. Patrick Rozenberg et.al [8] recommended the inclusion of the first-trimester ultrasound scan for the diagnosis of DS in addition to the second-trimester ultrasound. Detection of the ultrasonographic marker like nuchal fold, biparietal diameter and femur length is a challenging task due to the presence of speckle noise in an ultrasound image. Detection of these markers requires highly skilled sonologists, obstetricians, and fetal medicine professionals [2]. This led to the paradigm shift for the automated diagnosis of DS from the ultrasound images.
\nCuckle et al. proposed a genetic scan mechanism called nuchal translucency scan [9] to assess the risk of the fetus suffering from DS based on the nuchal fold. NT scans works based on the principle that “fetus with DS will accumulate more fluid at the end of the neck”. NT scan is always performed between 11 to 13 weeks of the pregnancy. As the risk of Aneuploidy and DS increases exponentially with the thickness of NT thickness [10], there has been wide growth in the mechanism of semi-automated methods for detecting DS based on the nuchal fold.
\nYinhui Deng et al. [11] have presented a systematic structural model for the automated detection of nuchal translucency region. Anzalone et al. [12] automated the measurement of nuchal translucency from the ultrasound image. Nirmala. S and Palanisamy. V [13] proposed the identification of nuchal translucency utilising the imaging methods like mean shift analysis and canny operators. The thickness of nuchal translucency was computed utilising blob analysis. Sonia. R and Shanthi V [14] performed the morphological operations and Otsu thresholding for the partitioning and calculating the region of nuchal translucency.
\nLai K Wee et.al [15] utilised the neural network to figure out the area of nuchal translucency. The boundary region of nuchal translucency layer is detected utilising an instinctive computerised algorithm. After the identification of the boundary region, the optimum thickness of the region is computed based on intensity continuity and edge strength. Yan Du et.al [16] performed fetal karyotype assessment among Chinese ladies and found that nonappearance of nasal bone in the second trimester has the high risk of DS in the fetus. Iliescu Dominic-Gabriel and Drăgușin Roxana-Cristina [17] reviewed various schemes in early hereditary screening and ultrasound assessment and inferred a decision that first-trimester ultrasound assessment can possibly analyse the most of fetal irregularities.
\nShafia Shakoor, Humera Ismail and Shama Munim [18] explored the experimental results in fetuses with EIF. The trial was experimented in Pakistan and the outcomes showed that 95.77% of the fetuses were normal, just 4.2% recorded abnormalities in heart and 0% had DS. Aaron B. Caughey, Deirdre J. Lyell et.al [19] assessed the impact of isolated EIF for screening DS. The investigation affirmed the use of EIF as a screening factor may prompt an immense number of amniocenteses and miscarriages to decide a rare DS fetus.
\nThe following observations are evident from the recent researches carried out in this field I) The most often utilised parameters for the identification of DS are nuchal fold, femur length and nasal bone hypoplasia ii) Association of DS and EIF is relatively low iii) There exist a need and necessity for developing a computerised methodology for spotting DS, as the manual diagnosis requires some artifacts.
\nHistopathology is the assessment of tissues in the impacted organs of the body. The extensive growth of the computerised image processing has given rise to a new diagnostic methodology termed as computer-assisted diagnosis (CAD). CAD assists the radiologists for disease detection, diagnosis and prognosis prediction. Diagnosis of DS based on EIF is one of the difficult issues that could be tackled by CAD methodology. The efficiency of imaging techniques and CAD can be unitedly employed to substitute the traditional DS diagnostic methods like amniocentesis and chronic sampling.
\nThe proposed system “Ultrasonic Detection of Down Syndrome using Multiscale Quantiser with Convolution Neural Network” uses a medical parameter EIF for the detection of DS. It is designed with the aim of discriminating the normal EIF from DS related EIF. It consists of two phases i) training phase and ii) testing phase. Training phase involves learning and forming the knowledge cluster of EIF related to DS. Testing phase involves classifying the diagnostic ultrasound fetal image as DS positive or negative, based on the knowledge cluster. An architecture diagram of the proposed system is given below in Figure 4.
\nThe architecture of the proposed system.
Not all the fetus that contain EIF result in DS, only few patterns of EIF can cause DS. The aim of this phase is to find the characteristics of DS-related EIF. The training phase includes five steps i) Image Pre-Processing using Neuro-Fuzzy Filter – to eliminate the speckle noise in the ultrasound fetal image that can hinder the detection of EIF ii) Detection of Nasal Bone using CCT - to confirm the presence of DS in the ultrasound fetal image iii) Detection of EIF using MSQCNN – to detect/segment EIF present in the ultrasound fetal image iv) Learning the features of EIF and cluster formation – to analyse the features of EIF like size, location and form a cluster. The systematic representation of the training phase is shown in Figure 5.
\nA schematic representation of the training phase.
The ultrasound devices produce high quality, real-time images but very often it is degraded with speckle noise. Speckle noise is a big threat for the quality of the results of image processing. It can produce artificial edges, echoes the patterns in the images that disturb the diagnosis process.
\nThe image pre-processing is the most significant step of the training phase. In pre-processing; a neuro-fuzzy filter that operates based on neuro-fuzzy and evolutionary learning is used to eliminate the speckle noise. The neural network possesses the ability to train. The knowledge for the training is fed into the system in a fuzzy format. Speckle noise is eliminated based on the fuzzy knowledge. A memetic algorithm is utilised to improve neuro-fuzzy filter. The algorithmic description of neuro-fuzzy filter is given below:
\nAlgorithm: Neuro-fuzzy filter Input: Ultrasound image with speckle noise Output: Processed image with less noise Algorithm \n
| \n
Detection of nasal bone is the second step of the training phase. The presence of DS is confirmed by the absence of nasal bone using CCT, as seventy percent of DS foetuses have no nasal bone [4]. CCT is a simple matching algorithm that works based on the correlation. The algorithmic description of CCT is displayed below [20].
\n1. Start 2. Consider target image S and template image T. 3. Consider the ultrasound image S for which nasal bone has to be detected \n \n \n\n \n\n \n\n 4. Consider the ultra sound image T with nasal bone detected \n \n \n\n5. Derive subset image(I) in T image that include nasal bone area 6. Look for I in S image 7. If I is present in S, obtain the location of I 8. Find cross correlation matrix for the template image and target image \n \n where \n 9. The correlation matrix is converted into surface plotting graph 10. Based on the graph, the maximum value of image correlation will be used for the detection of nasal bone 11. If the peak of the graph is less than 0.35, it can be concluded that nasal bone is absent 12. Stop | \n
Detection of EIF is the third step in the training phase following the image pre-processing and detection of the nasal bone. Training phase continues to the detection of EIF, only when the detection of nasal bone reports that fetal image has no nasal bone or weak nasal bone in it.
\nMSQNN is used to detect the EIF in the ultrasound images. MSQNN consists of additional components such as acceptor, quantiser with the conventional five layers that include convolution layer 1 and 2, pooling layer 1 and 2, fully connected layer 1 and 2. The output object coming out of FC Layer 2 is led into acceptor. The functionality of acceptor is to compare the object detected in the various scale of the input image and compute the difference between the objects. If the difference between the objects detected at the various scale lies within the threshold, it is treated as the final object. Acceptor computes the difference between the objects using the following formula.
\nwhere ∂ = Difference between the object detected at scale 1 and scale 2,
\nY (i) = Object detected at scale 1,
\nY (i + 1) = Object detected at scale 2,
\nSF = Scale factor at which object is detected.
\nAcceptor stops forwarding the control to Quantiser if the acceptance criteria are met. Below is the condition for the acceptance criteria to be met.
\nwhere
∂ = actual difference between objects detected at various scale.
\nIf the acceptance criterion is not met, acceptor transfers the control to quantiser. Quantiser performs quantisation by dividing the input image by scaling factor and rounding this value to the nearest integer. The mathematical formulation of quantiser is presented below.
\nThe next iteration for object detection starts with the reduced size input image. This iterative process of detecting an object continues until the number of iterations reaches the maximum or the acceptance criterion is met in Acceptor. The final object detected is treated as the EIF present in the ultrasound image of the fetus.
\nThe pictorial representation of Multiscale Quantised Convolution Neural Network is given below in Figure 6.
\nA pictorial representation of multiscale quantised convolution neural network.
Learning the features of EIF is the last step of the training phase, it happens just when the nasal bone is absent. The size and spatial coordinates of EIF are stored and clustered based on the closeness of the appearance (2). The similarity parameter utilised for clustering are i) size of EIF and ii) position of EIF specifically left ventricle and right ventricle. These clusters become the knowledge base for ELVQ function of the testing stage. Basically, two clusters are formed in this training phase of ELVQ, one cluster is for DS positive EIF fetus and other is for DS negative EIF fetus.
\nTesting phase involves discriminating DS related EIF from the normal EIF. The knowledge attained in the training phase plays the critical role in the determination of DS-related EIF. ELVQ maps the testing image to the cluster map developed in the training phase, by spotting the malignant EIF.
\nThe testing phase contains five steps namely i) Image Pre-Processing using Neuro-Fuzzy Filter – to eliminate the speckle noise in the ultrasound fetal image that can hinder the detection of EIF ii) Detection of EIF using MSQCNN – to detect EIF present in the ultrasound fetal image iii) Find the features of EIF – to find the features of EIF like size and location iv) Enhanced Learning Vector Quantisation Classification – to classify fetus into DS positive or negative based on the knowledge cluster. The flowchart representation of the testing phase is shown in Figure 7.
\nA flowchart representation of the testing phase.
The first two steps of testing phase are i) Image Pre-Processing and ii) Detection of EIF using MSQCNN are discussed well in the section 3.1.1 and 3.1.3. The third step concentrates on obtaining the features of EIF. Then as the fourth step, ELVQ classification involves.
\nELVQ supervised learning classifies the test image into DS positive or negative groups. For a single testing, two steps of ELVQ should be finished. In the initial step, the size of EIF is treated as the weight vector and in the second the location of EIF is treated as the weight vector (2). Enhanced Learning Vector Quantiser differs from the conventional Learning Vector Quantiser in one aspect; LVQ uses Euclidean distance whereas ELVQ uses Manhattan distance function.
\n\n \n
| \n
At the end of this testing phase, ELVQ classifies an input EIF fetus image as DS positive or negative.
\nThis section describes the experimental results of the proposed techniques. The proposed system is implemented with software Scilab. It [2] was employed for image and mathematical processing needs. Scilab is open source and freeware that can perform wide numerical computation for engineering and scientific applications.
\nAs there is no standard dataset for EIF fetus images, the experiment was carried out on the user created the dataset. Nearly 35 2D-ultrasound fetal images were collected from numerous online and offline sources. These fetal images belong to the period of 24–26 weeks. Out of these 35 fetal images, the diagnosis result of DS is known for 25 fetal images (that includes 20 ultrasound fetal images with DS positive and 5 ultrasound fetal images with DS negative).
\nThese 25 ultrasound fetal images are used in the training phase to obtain the characteristics of EIF associated with DS. There were also 10 ultrasound fetal images with EIF with no clue for DS were used in the testing phase. Some of the fetal images with EIF that were used for the experiment were shown in Figure 8.
\nThe dataset of the fetal images with EIF.
Training was performed with 25 DS diagnosed images. It was done in five iterations and the learning rate was consistently improving. The training phase results were presented in Table 1. The average rate of the learning at the end of training phase was 80%, while the rejection rate was only 20%.
\nIteration No# | \nNo. Of Input Images | \nLearning Rate | \nRejection Rate | \n
---|---|---|---|
1 | \n10 | \n10 | \n0 | \n
2 | \n20 | \n14 | \n6 | \n
3 | \n40 | \n34 | \n6 | \n
4 | \n80 | \n68 | \n12 | \n
5 | \n100 | \n86 | \n14 | \n
Training phase results.
Experimental results of the training phase are given as follows:
\nDetection of EIF using MSQCNN is shown in Figure 9.
\nDetection of EIF.
Detection of nasal bone using CCT in Figure 10.
\nDetection of nasal bone.
The fetal image (A. Normal) that was rejected and the fetal image (B. Down syndrome) that was learnt in the training phase is shown in Figure 11.
\nResults of the training phase.
The knowledge attained from the training phase about the location and size of EIF is shown in Table 2.
\nS.No | \nSize of EIF | \nLocation of EIF | \nOther Ultrasound findings | \nDS Positive | \n
---|---|---|---|---|
1 | \n> = 5.4 mm | \nLeft Ventricle | \nNasal Bone Absence | \nYes | \n
2 | \n> = 5 mm | \nBiventricular | \nNasal Bone Absence | \nYes | \n
Clustered knowledge from the training phase.
The testing phase was carried out to identify DS in 5 fetal images with EIF is shown in Table 3.
\nExperiment No # | \nTested images | \nDown Syndrome Positive | \nDown Syndrome Negative | \n
---|---|---|---|
1 | \n50 | \n33 | \n17 | \n
Results of the testing phase.
The fetal image with EIF that was analysed for DS in the testing phase and proved to be Down syndrome negative is displayed in Figure 12.
\n26 weeks fetus with left ventricular EIF with DS negative in the testing phase.
The fetal image with EIF that was analysed for DS in the testing phase and confirmed as Down syndrome positive is displayed in Figure 13.
\nA fetus with EIF on both the ventricles with DS positive in testing phase. X.
The performance of DS detection system is evaluated using parameters such as sensitivity, specificity, and accuracy. Sensitivity is the number of true positives that are rightly acknowledged by a diagnostic test [2]. It states how well the diagnostic test is identifying a disease.
\nSpecificity is the quantity of the true negatives rightly acknowledged by a diagnostic test [2]. It determines how good the test is identifying normal (negative) condition.
\nAccuracy is the number of true results, either true positive or true negative, in a population. It measures the degree of veracity of a diagnostic test on a condition.
\n(Note: T.P stands for True Positive, T.N stands for True Negative, F.P stands for False Positive, F.N stands for False Negative)
\nThe performance metrics of the proposed system is compared with the existing system “Automatic Computerized Diagnostic Tool for Down Syndrome Detection in Fetus” [2] is shown in Figure 14.
\nComparison with the state of art.
The proposed system showed the better performance than the existing system terms of sensitivity and accuracy, but it showed low specificity than the existing system.
\nThe newly designed system was able to clearly differentiate DS related fetus from the normal fetus based on EIF. It was producing very accurate results when operated on the fetal ultrasound images with a single EIF and multiple EIF. In future works, the soft markers like nuchal fold and femur length can be considered as an alternate parameter instead of nasal bone hypoplasia in the training phase.
\nThis chapter presented a new idea “Ultrasonic Detection of Down Syndrome using Multiscale Quantiser with Convolutional Neural Network” to detect DS based on EIF in an ultrasonic automated method. The proposed system was intelligent enough to clearly distinguish DS causing EIF from the normal EIF. It attained better results in terms of accuracy, sensitivity, and specificity. In future works, this system can be added as a new feature in the ultrasound fetal scan. It can also serve as an alternate for the conventional DS diagnostics like amniocentesis, PUBS, and CVS.
\nI feel proud and privileged to thank my guide Dr.A.R.Kavitha M.E., Ph.D. for her precious guidance and whole hearted support. I wish to express a sense of gratitude to my lovable English Teacher Mrs. Sucy George M.A., M.A., B.Ed for copy editing of this chapter. Last but not least I wish to dedicate this chapter to my family members (Late Simon – My Father, Mrs. Shanthi Simon - My Mother, Mrs. Gnana Rita M.A., B.Ed. - My Sibling, Mrs. Pearlin M.E - My Wife) and well-wishers (Mrs.Vimala Gilbert, Rev.Fr.Yesu Antony, Ms.Tamil Ilakika) for their boundless honest support and consistent motivation.
\nThe Edited Volume, also known as the IntechOpen Book, is an IntechOpen pioneered publishing product. Edited Volumes make up the core of our business - and as pioneers and developers of this Open Access book publishing format, we have helped change the way scholars and scientists publish their scientific papers - as scientific chapters.
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\\n\\nOut of all of the publishing options available to researchers, why choose to contribute your research to an IntechOpen Edited Volume? The reasons are simple. IntechOpen has worked exceptionally hard over the past years to fine tune the Open Access book publishing process and we continue to work hard to deliver the best for all of our contributors. The quality of published content is of utmost importance to us, followed closely by speed, and of course, availability and accessibility. To view current Open Access book projects that are Open for Submissions visit us here.
\\n\\nQUALITY CONTENT
\\n\\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
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\\n\\nWith regular submission for coverage in the single most important database, the Book Citation Index in the Web of Science™ Core Collection (BKCI), and no rejected submissions to date, over 43% of all Open Access books indexed in the BKCI are IntechOpen published books.
\\n\\nIn addition to BKCI, IntechOpen covers a number of important discipline specific databases as well, such as Thomson Reuters’ BIOSIS Previews.
\\n\\nACCESS
\\n\\nThe need for up to date information available at the click of a mouse is one thing that sets IntechOpen apart. By developing our own technologies in order to streamline the publishing process, we are able to minimize the amount of time from initial submission of a manuscript to its final publication date, without compromising the rigor of the editorial and peer review process. This means that the research published stays relevant, and in this fast paced world, this is very important.
\\n\\nYOUR WORK, YOUR COPYRIGHT
\\n\\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\\n\\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
\\n\\nOur Open Access book collection includes:
\\n\\n3,332 OPEN ACCESS BOOKS
\\n\\n107,564 INTERNATIONAL AUTHORS AND ACADEMIC EDITORS
\\n\\n113+ MILLION DOWNLOADS
\\n\\nPUBLISHING PROCESS STEPS
\\n\\nSee a complete overview of all publishing process steps and descriptions here.
\\n\\nCURRENT PROJECTS
\\n\\nTo view current Open Access book projects that are Open for Submissions visit us here.
\\n\\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'WHY PUBLISH IN AN INTECHOPEN EDITED VOLUME?
\n\nOut of all of the publishing options available to researchers, why choose to contribute your research to an IntechOpen Edited Volume? The reasons are simple. IntechOpen has worked exceptionally hard over the past years to fine tune the Open Access book publishing process and we continue to work hard to deliver the best for all of our contributors. The quality of published content is of utmost importance to us, followed closely by speed, and of course, availability and accessibility. To view current Open Access book projects that are Open for Submissions visit us here.
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\n\nOver the years we have learned what is important. What makes a difference to the researchers that work with us, what they value. Something that is very high not only on their lists, but our own, is the quality of the published content.
\n\nOur books contain scientific content written by two Nobel Prize winners, two Breakthrough Prize winners and 73 authors who are in the top 1% Most Cited.
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\n\nIn addition to BKCI, IntechOpen covers a number of important discipline specific databases as well, such as Thomson Reuters’ BIOSIS Previews.
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\n\nThe utilization of CC licenses allow researchers to retain copyright to their work. Researchers are free to use, adapt and share all content they publish with us. You will never have to pay permission fees to reuse a part of an experiment that you worked so hard to complete and are free to build upon your own research and the research of others. The Edited Volume helps bring together research from all over the world and compiles that research into one book - accessible for all. The research presented in chapter one can inspire the author of chapter three to take his or her research to the next level. It is about sharing ideas, insights and knowledge.
\n\nCan collaboration be inspired by a publishing format? At IntechOpen, the answer is yes. The way the research is published, the way it is accessed, it’s all part of our mission to help academics make a greater impact by giving readers free access to all published work.
\n\nOur Open Access book collection includes:
\n\n3,332 OPEN ACCESS BOOKS
\n\n107,564 INTERNATIONAL AUTHORS AND ACADEMIC EDITORS
\n\n113+ MILLION DOWNLOADS
\n\nPUBLISHING PROCESS STEPS
\n\nSee a complete overview of all publishing process steps and descriptions here.
\n\nCURRENT PROJECTS
\n\nTo view current Open Access book projects that are Open for Submissions visit us here.
\n\nNot sure if this is the right publishing option for you? Feel free to contact us at book.department@intechopen.com.
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