International Journal of Nonlinear Analysis and Applications
Automated prediction of endometriosis using deep learning
Document Type : Research Paper
Authors
Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, India.
Abstract
Endometriosis is the anomalous progress of cells at the outer part of the uterus. Generally, this endometrial tissue stripes the uterine cavity. The existence of endometriosis is identified through procedures known as Transvaginal Ultra Sound Scan (TVUS), Magnetic Resonance Imaging (MRI), Laparoscopic procedures, and Histopathological slides. Minimal Invasive Surgery (MIS) Laparo-scopic images are recorded in a small camera. To assist the surgeon in identifying their presence of endometriosis, image quality (characteristics) was enhanced for more visual clarity. Deep learning has the ability in recognising the images for classification. The Convolutional Neural Networks (CNNs) perform classification of images on large datasets. The proposed system evaluates the performance by a novel approach that implements the transfer learning model on a well-known architecture called ResNet50. The proposed system train the model on ResNet50 architecture and yielded a training accuracy of 91%, validation accuracy of 90%, precision of 83%, and recall of 82%, which can be applied for larger datasets with better performance. The presented system yields higher Area Under Curve (AUC) of about 0.78. The proposed method yields better performance using ResNet50 compared to other transfer learning techniques.
Keywords
[1] M. Ahmed, J.F. Al-Khafaji, C.A. Class, W. Wei, P. Ramalingam, H. Wakkaa, P.T. Soliman, M. Frumovitz, R.B.
Iyer and P.R. Bhosale, Can MRI help assess aggressiveness of endometrial cancer?, Clin. Radio. 73(9) (2018).
[2] S. Akter, D. Xu, S.C. Nagel and T. Joshi, A data mining approach for biomarker discovery using transcriptomics
in endometriosis, IEEE Int. Conf. Bioinf. Biomed. Madrid, Spain, (2018) 969–972.
[3] A.A.M. Al-Saffar, H. Tao and M.A. Talab, Review of deep convolution neural network in image classification,
Int. Conf. Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), Jakarta, Indonesia,
(2017) 26–31.
[4] J. Avaneesh, J. Thangakumar, T. Sudalaimuthu, P. Ranjana, N.S. Prakash and N.V.V. Sai Teja, Accurate object
detection with YOLO, Int. J. Pharm. Res. 12(1) (2020) 1418–1420.
[5] A. Bashar, Survey on evolving deep learning neural network architectures, J. Artif. Intel. Capsule Networks 1(2)
(2019) 73–82.[6] J. Bouaziz, R. Mashiach, S. Cohen, A. Kedem, A. Baron, M. Zajicek, I. Feldman, D. Seidman and D. Soriano,
How artificial intelligence can improve our understanding of the genes, associated with endometriosis: natural
language processing of the PubMed database, Biomed Res. Int. 2018 (2018).
[7] G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, O’Reilly Media, Inc.,
2008.
[8] X. Chen, Y. Wang, M. Shen, B. Yang, Q. Zhou, Y. Yi, W. Liu, G. Zhang, G. Yang and H. Zhang, Deep learning
for the determination of myometrial invasion depth and automatic lesion, identification in endometrial cancer
MR imaging: a preliminary study in a single institution, Eur. Radio. 30(9) (2020) 4985–4994.
[9] R.Y. Choi, A.S. Coyner, J. Kalpathy-Cramer, M.F. Chiang and J.P. Campbell, Introduction to machine learning,
neural networks, and deep learning, Transl. Vision Sci. Tech. 9(14) 2020.
[10] A. C¸ inar and M. Yildirim, Detection of tumors on brain MRI images using the hybrid convolutional neural network
architecture, Med. Hypo. 139 (2020) 109684.
[11] A. Coutinho, L.K. Bittencourt, C.E. Pires, F. Junqueira, C.M.A. de Oliveira Lima, E. Coutinho, M.A. Domingues,
R.C. Domingues and E. Marchiori, MR imaging in deep pelvic endometriosis: a pictorial essay, Radio Graph.
31(2) (2011) 549–567.
[12] H.-C. Dong, H.-K. Dong, M.-H. Yu, Y.-H. Lin and C.-C. Chang, Using deep learning with convolutional neural
network Approach to identify the invasion depth of endometrial cancer in myometrium using MR images: A pilot
study, Int. J. Environ. Res. Public Health 17(16) (2020) 1–18.
[13] P.P.R. Filho, E.d.S. Rebou¸cas, L.B. Marinho, R.M. Sarmento, J.M.R.S. Tavares and V.H.C. de Albuquerque,
Analysis of human tissue densities: a new approach to extract features from medical images, Pattern Recog. Lett.
94 (2017) 211–218.
[14] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M.S. Lew, Deep learning for visual understanding: A review,
J. Neural Comput. 187 (2016) 27–48.
[15] D.J. Hemanth and J. Anitha, Image pre-processing and feature extraction techniques for magnetic resonance brain
image analysis, In: T. Kim, D. Ko, T. Vasilakos, A. Stoica, and J. Abawajy (eds) Computer Applications for
Communication, Networking, and Digital Contents, FGCN 2012, Communications in Computer and Information
Science, 350 (2012).
[16] R. Hong, W. Liu, D. DeLair, N. Razavian and D. Feny¨o, Predicting endometrial cancer subtypes and molecular
features from histopathology images using multi-resolution deep learning model, bioRxiv 2020 (2020) 1–25.
[17] Y. Huang, K. Duan, T. Koythong, N.M. Patil, D. Fan, J. Liu, Z. Guan and X. Guan, Application of robotic
single-site surgery with optional additional port for endometriosis: a single institution’s experience, J. Robotic
Surg. (2021).
[18] A. Kensert, P.J. Harrison and O. Spjuth, Transfer learning with deep convolutional neural networks for classifying
cellular morphological changes, SLAS Discov. 24(4) (2019) 466–475.
[19] A. Khan, A. Sohail, U. Zahoora and A.S. Qureshi, A survey of the recent architectures of deep convolutional
neural networks published in artificial intelligence review, Artif. Intell. Rev. 53 (2020) 5455–5516.
[20] E.J. Kleczyk, A. Peri, T. Yadav, R. Komera, M. Peri, V. Guduru, S. Amirtharaj and M. Huang, Predicting
endometriosis onset using machine learning algorithms, BMC Women’s Health 2021 (2021) 1–14.
[21] D.-H. Lee, Y. Li and B.-S. Shin, Mid-level feature extraction method based transfer learning to small-scale dataset
of medical images with visualizing analysis, J. Inf. Proc. Syst. 16(6) (2020) 1293–1308.
[22] A. Leibetseder, S. Kletz, K. Schoeffmann, S. Keckstein and J. Keckstein, GLENDA: gynaecologic laparoscopy
endometriosis dataset, In: Y. Ro, et al. (eds) MultiMedia Modeling, MMM 2020. Lecture Notes Comput. Sci.
11962 (2019) 439–450.
[23] G. Li, Y. Zhang, B. Xu and X. Li, Image analysis and processing of skin cell injury based on OpenCV, J. Phys.
Conf. Ser. 1237 (2019) 032003.
[24] T. Liu, S. Fang, Y. Zhao, P. Wang and J. Zhang, Implementation of training convolutional neural networks, arXiv
preprint arXiv:1506.01195, CoRR. (2015).
[25] C. Ma, S. Xu, X. Yi, L. Li and C. Yu, Research on image classification method based on DCNN, Int. Conf. Compu.
Engin. Appl. (ICCEA), (2020) 873–876.
[26] S. Madad Zadeh, T. Francois, L. Calvet, P. Chauvet, M. Canis, A. Bartoli and N. Bourdel, SurgAI: deep learning
for computerized laparoscopic image understanding in gynaecology, Surg. Endosc. 34(12) (2020) 5377–5383.
[27] P. Mascagni, A. Vardazaryan, D. Alapatt, T. Urade, T. Emre, C. Fiorillo, P. Pessaux, D. Mutter, J. Marescaux,
G. Costamagna, B. Dallemagne, and N. Padoy, Artificial intelligence for surgical safety automatic assessment
of the critical view of safety in laparoscopic cholecystectomy using deep learning annals of surgery, Ann. Surg.
(2020).
[28] P. Nahar, S. Tanwani and N.S. Chaudhari, Fingerprint classification using deep neural network model resnet50,Int. J. Res. Anal. Rev. 5(4) 2018 1521–1535.
[29] D.L. Olive, and E.A. Pritts, Treatment of Endometriosis, N. Engl. J. Med. 345(4) 2001 266–275.
[30] S. Perumal, and T. Velmurugan, Pre-processing by contrast enhancement techniques for medical images, Int. J.
Pure Appl. Math. 118(18) (2018) 3681–3688.
[31] A.M. Praiss, Y. Huang, C.M. St. Clair, A.I. Tergas, A. Melamed, F. Khoury-Collado, J.Y. Hou, J. Hu, C. Hur,
D.L. Hershman and J.D. Wright, Using machine learning to create prognostic systems for endometrial cancer,
Gynecol Oncol. 159(3) (2020) 744–750.
[32] S. P´erez-Fern´andez, P. Mart´ınez-Camblor, P., Filzmoser and N. Corral, Visualizing the decision rules behind the
ROC curves: understanding the classification process, AStA Adv. Stat. Anal. 105 (2021) 135-–161.
[33] A. Rohini, and T. SudalaiMuthu, Machine learning based analysis of influence propagation on social network
with time series analysis, 2020 Fourth International Conference on Inventive Systems and Control (ICISC),
Coimbatore, India, (2020) 57–61.
[34] L. Saba, S. Guerriero, R. Sulcis, M. Pilloni, S. Ajossa, G. Melis and G. Mallarini, MRI and ”tenderness guided”
transvaginal ultrasonography in the diagnosis of recto-sigmoid endometriosis, J. Magn. Reson. Imag. 35(2) (2012)
352–360.
[35] M. Sankupellay and D. Konovalov, Bird call recognition using deep convolutional neural network, ResNet-50,
Proc. Aust. Acoust. Soc. Conf. 134 (2018) 1–8.
[36] Y. Takahashi, K. Sone, K. Noda, K. Yoshida, Y. Toyohara, K. Kato, F. Inoue, A. Kukita, A. Taguchi, H. Nishida,
Y. Miyamoto, M. Tanikawa, T. Tsuruga, T. Iriyama, K. Nagasaka, Y. Matsumoto, Y. Hirota, O. Hiraike-Wada,
K. Oda, M. Maruyama, Y. Osuga and T. Fujii, Automated system for diagnosing endometrial cancer by adopting
deep-learning technology in hysteroscopy, PLOS ONE 16(3) (2021).
[37] Y. D. Wang, M. Shabaninejad, R.T. Armstrong and P. Mostaghimi, Deep neural networks for improving physical
accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images, Appl. Soft Comput. 104 (2021)
107185.
[38] C.E. Widodo, K. Adi and R. Gernowo, Medical image processing using python and open cv, Journal of Physics:
Conference Series, J. Phys. Conf. Ser. 1524 (2020).
[39] M. Xin, and Y. Wang, Research on image classification model based on deep convolution, EURASIP J. Image
Video Proc. 2019(1) (2019).
[40] Y.Z. Zhang, Z.H. Wang, J. Zhang, C. Wang, Y.S. Wang, H. Chen, L.H. Shan, J.N. Huo, J.H. Gu, and X. Ma,
Deep learning model for classifying endometrial lesions, J. Transl. Med. 19(1) (2021).
Iyer and P.R. Bhosale, Can MRI help assess aggressiveness of endometrial cancer?, Clin. Radio. 73(9) (2018).
[2] S. Akter, D. Xu, S.C. Nagel and T. Joshi, A data mining approach for biomarker discovery using transcriptomics
in endometriosis, IEEE Int. Conf. Bioinf. Biomed. Madrid, Spain, (2018) 969–972.
[3] A.A.M. Al-Saffar, H. Tao and M.A. Talab, Review of deep convolution neural network in image classification,
Int. Conf. Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), Jakarta, Indonesia,
(2017) 26–31.
[4] J. Avaneesh, J. Thangakumar, T. Sudalaimuthu, P. Ranjana, N.S. Prakash and N.V.V. Sai Teja, Accurate object
detection with YOLO, Int. J. Pharm. Res. 12(1) (2020) 1418–1420.
[5] A. Bashar, Survey on evolving deep learning neural network architectures, J. Artif. Intel. Capsule Networks 1(2)
(2019) 73–82.[6] J. Bouaziz, R. Mashiach, S. Cohen, A. Kedem, A. Baron, M. Zajicek, I. Feldman, D. Seidman and D. Soriano,
How artificial intelligence can improve our understanding of the genes, associated with endometriosis: natural
language processing of the PubMed database, Biomed Res. Int. 2018 (2018).
[7] G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, O’Reilly Media, Inc.,
2008.
[8] X. Chen, Y. Wang, M. Shen, B. Yang, Q. Zhou, Y. Yi, W. Liu, G. Zhang, G. Yang and H. Zhang, Deep learning
for the determination of myometrial invasion depth and automatic lesion, identification in endometrial cancer
MR imaging: a preliminary study in a single institution, Eur. Radio. 30(9) (2020) 4985–4994.
[9] R.Y. Choi, A.S. Coyner, J. Kalpathy-Cramer, M.F. Chiang and J.P. Campbell, Introduction to machine learning,
neural networks, and deep learning, Transl. Vision Sci. Tech. 9(14) 2020.
[10] A. C¸ inar and M. Yildirim, Detection of tumors on brain MRI images using the hybrid convolutional neural network
architecture, Med. Hypo. 139 (2020) 109684.
[11] A. Coutinho, L.K. Bittencourt, C.E. Pires, F. Junqueira, C.M.A. de Oliveira Lima, E. Coutinho, M.A. Domingues,
R.C. Domingues and E. Marchiori, MR imaging in deep pelvic endometriosis: a pictorial essay, Radio Graph.
31(2) (2011) 549–567.
[12] H.-C. Dong, H.-K. Dong, M.-H. Yu, Y.-H. Lin and C.-C. Chang, Using deep learning with convolutional neural
network Approach to identify the invasion depth of endometrial cancer in myometrium using MR images: A pilot
study, Int. J. Environ. Res. Public Health 17(16) (2020) 1–18.
[13] P.P.R. Filho, E.d.S. Rebou¸cas, L.B. Marinho, R.M. Sarmento, J.M.R.S. Tavares and V.H.C. de Albuquerque,
Analysis of human tissue densities: a new approach to extract features from medical images, Pattern Recog. Lett.
94 (2017) 211–218.
[14] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M.S. Lew, Deep learning for visual understanding: A review,
J. Neural Comput. 187 (2016) 27–48.
[15] D.J. Hemanth and J. Anitha, Image pre-processing and feature extraction techniques for magnetic resonance brain
image analysis, In: T. Kim, D. Ko, T. Vasilakos, A. Stoica, and J. Abawajy (eds) Computer Applications for
Communication, Networking, and Digital Contents, FGCN 2012, Communications in Computer and Information
Science, 350 (2012).
[16] R. Hong, W. Liu, D. DeLair, N. Razavian and D. Feny¨o, Predicting endometrial cancer subtypes and molecular
features from histopathology images using multi-resolution deep learning model, bioRxiv 2020 (2020) 1–25.
[17] Y. Huang, K. Duan, T. Koythong, N.M. Patil, D. Fan, J. Liu, Z. Guan and X. Guan, Application of robotic
single-site surgery with optional additional port for endometriosis: a single institution’s experience, J. Robotic
Surg. (2021).
[18] A. Kensert, P.J. Harrison and O. Spjuth, Transfer learning with deep convolutional neural networks for classifying
cellular morphological changes, SLAS Discov. 24(4) (2019) 466–475.
[19] A. Khan, A. Sohail, U. Zahoora and A.S. Qureshi, A survey of the recent architectures of deep convolutional
neural networks published in artificial intelligence review, Artif. Intell. Rev. 53 (2020) 5455–5516.
[20] E.J. Kleczyk, A. Peri, T. Yadav, R. Komera, M. Peri, V. Guduru, S. Amirtharaj and M. Huang, Predicting
endometriosis onset using machine learning algorithms, BMC Women’s Health 2021 (2021) 1–14.
[21] D.-H. Lee, Y. Li and B.-S. Shin, Mid-level feature extraction method based transfer learning to small-scale dataset
of medical images with visualizing analysis, J. Inf. Proc. Syst. 16(6) (2020) 1293–1308.
[22] A. Leibetseder, S. Kletz, K. Schoeffmann, S. Keckstein and J. Keckstein, GLENDA: gynaecologic laparoscopy
endometriosis dataset, In: Y. Ro, et al. (eds) MultiMedia Modeling, MMM 2020. Lecture Notes Comput. Sci.
11962 (2019) 439–450.
[23] G. Li, Y. Zhang, B. Xu and X. Li, Image analysis and processing of skin cell injury based on OpenCV, J. Phys.
Conf. Ser. 1237 (2019) 032003.
[24] T. Liu, S. Fang, Y. Zhao, P. Wang and J. Zhang, Implementation of training convolutional neural networks, arXiv
preprint arXiv:1506.01195, CoRR. (2015).
[25] C. Ma, S. Xu, X. Yi, L. Li and C. Yu, Research on image classification method based on DCNN, Int. Conf. Compu.
Engin. Appl. (ICCEA), (2020) 873–876.
[26] S. Madad Zadeh, T. Francois, L. Calvet, P. Chauvet, M. Canis, A. Bartoli and N. Bourdel, SurgAI: deep learning
for computerized laparoscopic image understanding in gynaecology, Surg. Endosc. 34(12) (2020) 5377–5383.
[27] P. Mascagni, A. Vardazaryan, D. Alapatt, T. Urade, T. Emre, C. Fiorillo, P. Pessaux, D. Mutter, J. Marescaux,
G. Costamagna, B. Dallemagne, and N. Padoy, Artificial intelligence for surgical safety automatic assessment
of the critical view of safety in laparoscopic cholecystectomy using deep learning annals of surgery, Ann. Surg.
(2020).
[28] P. Nahar, S. Tanwani and N.S. Chaudhari, Fingerprint classification using deep neural network model resnet50,Int. J. Res. Anal. Rev. 5(4) 2018 1521–1535.
[29] D.L. Olive, and E.A. Pritts, Treatment of Endometriosis, N. Engl. J. Med. 345(4) 2001 266–275.
[30] S. Perumal, and T. Velmurugan, Pre-processing by contrast enhancement techniques for medical images, Int. J.
Pure Appl. Math. 118(18) (2018) 3681–3688.
[31] A.M. Praiss, Y. Huang, C.M. St. Clair, A.I. Tergas, A. Melamed, F. Khoury-Collado, J.Y. Hou, J. Hu, C. Hur,
D.L. Hershman and J.D. Wright, Using machine learning to create prognostic systems for endometrial cancer,
Gynecol Oncol. 159(3) (2020) 744–750.
[32] S. P´erez-Fern´andez, P. Mart´ınez-Camblor, P., Filzmoser and N. Corral, Visualizing the decision rules behind the
ROC curves: understanding the classification process, AStA Adv. Stat. Anal. 105 (2021) 135-–161.
[33] A. Rohini, and T. SudalaiMuthu, Machine learning based analysis of influence propagation on social network
with time series analysis, 2020 Fourth International Conference on Inventive Systems and Control (ICISC),
Coimbatore, India, (2020) 57–61.
[34] L. Saba, S. Guerriero, R. Sulcis, M. Pilloni, S. Ajossa, G. Melis and G. Mallarini, MRI and ”tenderness guided”
transvaginal ultrasonography in the diagnosis of recto-sigmoid endometriosis, J. Magn. Reson. Imag. 35(2) (2012)
352–360.
[35] M. Sankupellay and D. Konovalov, Bird call recognition using deep convolutional neural network, ResNet-50,
Proc. Aust. Acoust. Soc. Conf. 134 (2018) 1–8.
[36] Y. Takahashi, K. Sone, K. Noda, K. Yoshida, Y. Toyohara, K. Kato, F. Inoue, A. Kukita, A. Taguchi, H. Nishida,
Y. Miyamoto, M. Tanikawa, T. Tsuruga, T. Iriyama, K. Nagasaka, Y. Matsumoto, Y. Hirota, O. Hiraike-Wada,
K. Oda, M. Maruyama, Y. Osuga and T. Fujii, Automated system for diagnosing endometrial cancer by adopting
deep-learning technology in hysteroscopy, PLOS ONE 16(3) (2021).
[37] Y. D. Wang, M. Shabaninejad, R.T. Armstrong and P. Mostaghimi, Deep neural networks for improving physical
accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images, Appl. Soft Comput. 104 (2021)
107185.
[38] C.E. Widodo, K. Adi and R. Gernowo, Medical image processing using python and open cv, Journal of Physics:
Conference Series, J. Phys. Conf. Ser. 1524 (2020).
[39] M. Xin, and Y. Wang, Research on image classification model based on deep convolution, EURASIP J. Image
Video Proc. 2019(1) (2019).
[40] Y.Z. Zhang, Z.H. Wang, J. Zhang, C. Wang, Y.S. Wang, H. Chen, L.H. Shan, J.N. Huo, J.H. Gu, and X. Ma,
Deep learning model for classifying endometrial lesions, J. Transl. Med. 19(1) (2021).
)
Volume 12, Issue 2
November 2021Pages 2403-2416
- Receive Date: 03 May 2021
- Revise Date: 27 May 2021
- Accept Date: 25 June 2021