State of the Art in Neural Networks and Their Applications: Volume 1

310 Pages·2021·15.58 MB·English

by  Ayman S. El-Baz, Jasjit S. Suri

State of the Art in
Neural Networks and
Their Applications
State of the Art in
Neural Networks and
Their Applications
Volume 1
Edited by
Ayman S. El-Baz
University of Louisville, Louisville, Kentucky, United States;
University of Louisville at AlAlamein International University
(UofL-AIU), New Alamein City, Egypt
Jasjit S. Suri
AtheroPoint LLC, Roseville, CA, United States
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With love and affection to my mother and father,
whose loving spirit sustains me still
—Ayman S. El-Baz
To my late loving parents, immediate family, and children
—Jasjit S. Suri
Contents
List ofContributors.................................................................................................xv
Biographies.............................................................................................................xxi
Acknowledgments...............................................................................................xxiii
CHAPTER 1 Computer-aided detection of abnormality in
mammography using deep object detectors................1
Pengcheng Xi,GhazalRouhafzay, Haitao Guan,
Chang Shu, Louis Borgeat and Rafik Goubran
1.1 Introduction....................................................................................1
1.2 Literaturereview............................................................................2
1.3 Methodology...................................................................................3
1.3.1 Architecturesofdeep convolutional neural
networks anddeep object detectors....................................4
1.3.2 Abnormality detection withfaster R-convolutional
neuralnetworks...................................................................6
1.3.3 Abnormality detection withYOLO....................................7
1.4 Experimental results.......................................................................9
1.4.1 Data preparation..................................................................9
1.4.2 Abnormality detection withfaster R-convolutional
neuralnetworks.................................................................10
1.4.3 Abnormality detection withYOLO..................................10
1.4.4 Results comparison...........................................................13
1.5 Discussions...................................................................................13
1.6 Conclusion....................................................................................15
References....................................................................................16
CHAPTER 2 Detection of retinal abnormalities in fundus
image using CNN deep learning networks................19
Mohamed Akil, Yaroub Elloumi andRostom Kachouri
2.1 Introduction..................................................................................19
2.2 Earlierscreeningand diagnosis ofocular diseaseswith
CNN deep learning networks.......................................................25
2.2.1 Glaucoma..........................................................................25
2.2.2 Age-related macular degeneration....................................31
2.2.3 Diabetic retinopathy..........................................................36
2.2.4 Cataract.............................................................................42
vii
viii Contents
2.3 Deep learning(cid:1)based smartphone for detectionof
retinalabnormalities.....................................................................46
2.3.1 Smartphone-captured fundus image evaluation...............46
2.3.2 Deep learning(cid:1)based method ofocular pathology
detection fromsmartphone-captured fundus image.........47
2.4 Discussion.....................................................................................49
2.5 Conclusion....................................................................................52
References....................................................................................53
CHAPTER 3 A survey of deep learning-based methods for
cryo-electron tomography data analysis...................63
Xiangrui Zeng, Xiaoyan Yang, Zhenyu Wangand
Min Xu
3.1 Introduction..................................................................................63
3.2 Deep learning-based methods......................................................64
3.2.1 Detection and segmentation..............................................64
3.2.2 Classification.....................................................................66
3.2.3 Others................................................................................68
3.3 Conclusion....................................................................................69
References....................................................................................70
CHAPTER 4 Detection, segmentation, and numbering of
teeth in dental panoramic images with
mask regions with convolutional neural
network features.........................................................73
AyseBetulOktay and Anıl Gurses
4.1 Introduction..................................................................................73
4.2 Related work................................................................................75
4.3 Fe´de´ration Dentaire Internationale tooth numbering
system...........................................................................................78
4.4 The method...................................................................................78
4.4.1 Implementation details......................................................80
4.5 Experimental analysis..................................................................81
4.5.1 Dataset...............................................................................81
4.5.2 Evaluation.........................................................................83
4.5.3 Results...............................................................................84
4.6 Discussion and conclusions..........................................................86
References....................................................................................87
Contents ix
CHAPTER 5 Accurate identification of renal transplant
rejection: convolutional neural networks and
diffusion MRI...............................................................91
Mohamed Shehata, Hisham Abdeltawab,
Mohammed Ghazal, Ashraf Khalil,Shams Shaker,
Ahmed Shalaby, Ali Mahmoud, Mohamed Abou El-Ghar,
Amy C. Dwyer, MoumenEl-Melegy, Ashraf M.Bakr,
Jasjit S. Suriand Ayman S. El-Baz
5.1 Introduction..................................................................................91
5.2 Methods........................................................................................93
5.2.1 Kidneysegmentation........................................................94
5.2.2 Feature extraction..............................................................95
5.2.3 Renal transplant classification using deep
convolutional neuralnetwork...........................................96
5.3 Experimental results.....................................................................97
5.4 Conclusion..................................................................................100
Acknowledgments.....................................................................102
References..................................................................................102
CHAPTER 6 Applications of the ESPNet architecture in
medical imaging.......................................................117
SachinMehta, NicholasNuechterlein, Ezgi Mercan,
Beibin Li,Shima Nofallah, WenjunWu,XimingLu,
AnatCaspi, Mohammad Rastegari, Joann Elmore,
Hannaneh Hajishirzi andLindaShapiro
6.1 Introduction................................................................................117
6.2 Background.................................................................................118
6.2.1 Standard convolution......................................................118
6.2.2 Dilated convolution.........................................................118
6.3 The ESPNet architecture............................................................119
6.3.1 Efficient spatialpyramidunit.........................................119
6.3.2 Segmentation architecture...............................................120
6.4 Experimental results...................................................................122
6.4.1 Breastbiopsy whole slide image dataset........................123
6.4.2 Brain tumor segmentation...............................................127
6.4.3 Other applications...........................................................129
6.5 Conclusion..................................................................................130
Acknowledgment.......................................................................130
References..................................................................................130
x Contents
CHAPTER 7 Achievements of neural network in skin
lesions classification ...............................................133
NaziaHameed, Antesar Shabut, Fozia Hameed,
Silvia Cirstea and Alamgir Hossain
7.1 Introduction................................................................................133
7.2 Literature review........................................................................137
7.3 Background.................................................................................139
7.4 Dataset........................................................................................141
7.5 Methodology...............................................................................142
7.6 Results and discussion................................................................143
7.7 Conclusion..................................................................................148
References..................................................................................148
CHAPTER 8 A computer-aided diagnosis system for breast
cancer molecular subtype prediction in
mammographic images.............................................153
VivekKumar Singh, Hatem A. Rashwan,
Mohamed Abdel-Nasser, Farhan Akram,RamiHaffar,
NidhiPandey, MeritxellArenas, SantiagoRomaniand
Domenec Puig
8.1 Introduction................................................................................153
8.2 Background.................................................................................155
8.2.1 Breast cancer detection...................................................155
8.2.2 Breast tumor segmentation.............................................156
8.2.3 Shape classification.........................................................158
8.3 Datasets.......................................................................................159
8.4 Methodology...............................................................................160
8.4.1 Modified Faster R-CNN for breast tumor detection......160
8.4.2 Breast tumor segmentation using conditional
generative adversarialnetwork.......................................163
8.4.3 Shapedescriptorusingconvolutionalneuralnetwork.......170
8.4.4 Breast cancer molecular subtypes correlation to
the tumor shape...............................................................172
8.5 Conclusion..................................................................................173
References..................................................................................174
CHAPTER 9 Computer-aided diagnosis of renal masses............179
Fatemeh Zabihollahy,ErangaUkwatta and
NicolaSchieda
9.1 Introduction................................................................................179
9.2 Segmentation ofkidneys............................................................181
Contents xi
9.2.1 Convolutional neural network........................................182
9.2.2 U-Net...............................................................................185
9.2.3 Performance evaluation ofthe algorithm.......................185
9.2.4 Deeplearning(cid:1)based methods for automated
segmentation ofkidney...................................................186
9.3 Kidney tumor localization..........................................................186
9.4 Differentiation ofmalignant versus benign renal masses.........189
9.5 Future perspectives.....................................................................191
References..................................................................................192
CHAPTER 10 Early identification of acute rejection for renal
allografts: a machine learning approach................197
Mohamed Shehata, Fatma Taher, Mohammed Ghazal,
Shams Shaker, MohamedAbou El-Ghar,
Mohamed Badawy, Ahmed Shalaby, Maryam El-Baz,
Ali Mahmoud, Amy C. Dwyer, Ashraf M. Bakr,
Jasjit S. Suriand Ayman S. El-Baz
Acknowledgment.......................................................................197
10.1 Introduction................................................................................198
10.2 Methods......................................................................................199
10.2.1 Diffusion-weighted image markers..............................200
10.2.2 Clinical biomarkers.......................................................200
10.2.3 Integration process ofclinical with imaging
biomarkers.....................................................................200
10.3 Experimental results...................................................................201
10.4 Conclusion..................................................................................205
References..................................................................................206
CHAPTER 11 Deep learning for computer-aided diagnosis in
ophthalmology: a review..........................................219
JamesM. Brown and Georgios Leontidis
11.1 Introduction................................................................................219
11.1.1 Theburden ofeye disease............................................219
11.1.2 Imaging and image analysis.........................................219
11.1.3 Deeplearning: an emerging state-of-the-art.................221
11.2 Deep learning: the methods.......................................................222
11.2.1 Reference standards......................................................223
11.2.2 Preprocessing andaugmentation..................................224
11.2.3 Architectures,transfer learning,and ensembling.........225
11.2.4 Lossfunctionsand performance metrics......................226
xii Contents
11.3 Limitations ofthe state-of-the-art..............................................227
11.3.1 Trustworthiness andtransparency................................227
11.3.2 Uncertaintyestimation..................................................228
11.3.3 Explainability andinterpretability................................229
11.4 Beyondconvolutional neuralnetworks.....................................231
11.4.1 Generativeadversarial networks...................................231
11.4.2 Capsule networks..........................................................232
11.5 Conclusion..................................................................................233
References..................................................................................233
CHAPTER 12 Deep learning for ophthalmology using optical
coherence tomography.............................................239
HenryA. Leopold, Amitojdeep Singh,
Sourya Senguptaand Vasudevan Lakshminarayanan
12.1 Introduction................................................................................239
12.2 Optical coherence tomography..................................................239
12.2.1 Variations ofoptical coherence tomography
systems..........................................................................241
12.2.2 Optical coherence tomography datasets.......................244
12.2.3 Advantages and disadvantages ofoptical
coherence tomography imaging....................................245
12.3 Retinal biomarkersand diseases................................................246
12.3.1 Important retinaldiseases.............................................246
12.3.2 Biomarker use indisease analysis................................248
12.4 Traditional approaches for ophthalmicdiagnosis......................249
12.4.1 Image-processing fundamentals....................................250
12.4.2 Feature extraction fundamentals...................................250
12.4.3 Traditionalclassifiers....................................................250
12.4.4 Applications..................................................................251
12.5 Deep learningapproaches to optical coherence
tomography analysis...................................................................254
12.5.1 Convolutional neuralnetwork applications..................254
12.5.2 Autoencoder applications..............................................257
12.5.3 Generativeadversarial networkapplications................259
12.6 Final thoughts.............................................................................260
Acknowledgment.......................................................................260
Conflict ofinterest.....................................................................260
References..................................................................................260
Further reading..........................................................................269