Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image

Ndong Boucar; Djigo Mamoudou Salif; Mboup Mamadou Lamine; Tall Khaly; Bathily El Hadji Amadou Lamine; Diop Ousseynou; Akpo Géraud Léra Kelvin; Badji Nfally; Mbaye Gora; Diouf Augustin Louis Diaga; Sy Pape Mady; Djiboune Alphonse; Fashinan Herbert; Farssi Mohamed; Ndoye Oumar; Diarra Mounibé; Mbodji Mamadou

doi:doi:10.11648/j.ijmi.20210904.14

Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image

Ndong Boucar, Djigo Mamoudou Salif, Mboup Mamadou Lamine, Tall Khaly, Bathily El Hadji Amadou Lamine, Diop Ousseynou, Akpo Géraud Léra Kelvin, Badji Nfally, Mbaye Gora, Diouf Augustin Louis Diaga, Sy Pape Mady, Djiboune Alphonse, Fashinan Herbert, Farssi Mohamed, Ndoye Oumar, Diarra Mounibé, Mbodji Mamadou

Published in International Journal of Medical Imaging (Volume 9, Issue 4)

Received: 11 October 2021 Accepted: 2 November 2021 Published: 12 November 2021

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Abstract

Objective: The aim of this study was to describe the stages of learning Kohonen's self-organizing maps applied to scintigraphy imaging in order to perform classification for medical diagnostic aid. Method: To achieve these goals, the neurons, arranged on a regular grid, are connected to each other by a neighbor relationship, which creates the topology of the map. The input layer consisted of pixels from the scintigraphy images. Results: During the iteration rounds of learning, we have seen a deployment of neurons on the nodes of the map that becomes more and more important. And it is the same for the winning neurons. After 750 iterations, the Davies Bouldin index attests to the end of the training with a quantization error that goes from 0.175 at the beginning of the training to 0.0225 at the end of the training. After this study, we find that neurons 41, 62, 121, 101 and 145 have captured most of the data with a peak uptake achieved by neuron 41 which has captured 1048 data. This individualizes the class of high intensities undoubtedly corresponding to metastatic hyperfixations. Conclusion: This innovative method could undoubtedly be integrated into the link in the chain highlighting periarticular metastases in developing countries, most of which do not have a SPECT-CT.

Published in	International Journal of Medical Imaging (Volume 9, Issue 4)
DOI	10.11648/j.ijmi.20210904.14
Page(s)	189-192
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Artificial Neural Networks, Kohonen Self Organizing Maps, Bone Scintigraphy

References

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[2]	M. L. Mboup, K. Tall, B. Ndong, I. Diop, M. B. Gueye, F. Kaly, M. S. Camara, P. A. Mboup, M. Mbodj, S. M. Farssi “Système d’aide au Diagnostic des Métastases du Cancer de la Prostate par le traitement de l’Image de Scintigraphie Osseuse”, 1^ere Edition de la Journée Scientifique des Doctorants, COJEC, Ecole Supérieure Polytechnique (ESP), 11 Juin 2016, Dakar, Sénégal.
[3]	Dryfus G., Martinez J. M., Samuelides M. B., Gordon M. B., Badran F., Thiria S. Apprentissage statistique des réseaux de neurones, cartes topologiques, machine à vecteur supports, 2008.
[4]	Kohonen T. Self-organized formation of topologically correct feature Maps. Biological cybernetics springer-verla. 1982 vol. 43 pp. 59-69.
[5]	Guèye M. B. Inversion neuronale pour la reconstruction de profils de salinité océanique en atlantique tropical à partir de mesures de surface et de hauteurs d’eau, Thèse soutenue à l’université Pierre et Marie Curie, Paris, 2015 pp. 7-8.
[6]	Jainn- Shiun Chiu, Yuh-Feng Wang, Yu-Cheih Su, Ling-Huei Wei, Jian Guo Liao. Artificial Neural Network to predict skeletal Metastasis in patients with Prostate cancer, Journal of medical systems, vol. 33, Issue 2 April 2009, pp 91-100.
[7]	Rigau J. Tiguert, R. le Normand L., Karam G. Glimain P., Buzelin J. M. et al. Prognostic value of bone scan in patients with metastatic prostate cancer treated initially with androgen deprivation therapy J. urol. 168 (4pt 1): 1423-1426, 2002.
[8]	J. T. Batuello, E. J. Gamito, E. D. Crawford, M. Han, A. W. Partin, D. G. McLeod, et C. O’Donnell, «Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer», Urology, vol. 57, no 3, p. 481–485, 2001.
[9]	Kerkeni N. Classification des stades de sommeil par des réseaux de neurones artificiels hiérarchiques IRBM, 2012 vol. 33, pp. 25-40.
[10]	S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems, 2015.
[11]	A. J. Tatem, H. G. Lewis, P. M. Atkinson, et M. S. Nixon, «Super-resolution target identification from remotely sensed images using a Hopfield neural network», IEEE Trans. Geosci. Remote Sens., vol. 39, no 4, p. 781-796, 2001.
[12]	M. Negreiros et A. Palhano, «The capacitated centred clustering problem», Comput. Oper Res., vol. 33, n^o 6, p. 1639-1663, 2006.
[13]	C. L. Forgy, «Rete: A fast algorithm for the many pattern/many object pattern match problem», in Readings in Artificial Intelligence and Databases, Elsevier, 1989, p. 547-559.
[14]	Kohonen T. Self organizing maps, Berlin Heidelberg: Springer Verlag 2001 pp. 501 pages.

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APA Style

Ndong Boucar, Djigo Mamoudou Salif, Mboup Mamadou Lamine, Tall Khaly, Bathily El Hadji Amadou Lamine, et al. (2021). Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image. International Journal of Medical Imaging, 9(4), 189-192. https://doi.org/10.11648/j.ijmi.20210904.14

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ACS Style

Ndong Boucar; Djigo Mamoudou Salif; Mboup Mamadou Lamine; Tall Khaly; Bathily El Hadji Amadou Lamine, et al. Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image. Int. J. Med. Imaging 2021, 9(4), 189-192. doi: 10.11648/j.ijmi.20210904.14

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AMA Style

Ndong Boucar, Djigo Mamoudou Salif, Mboup Mamadou Lamine, Tall Khaly, Bathily El Hadji Amadou Lamine, et al. Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image. Int J Med Imaging. 2021;9(4):189-192. doi: 10.11648/j.ijmi.20210904.14

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@article{10.11648/j.ijmi.20210904.14,
  author = {Ndong Boucar and Djigo Mamoudou Salif and Mboup Mamadou Lamine and Tall Khaly and Bathily El Hadji Amadou Lamine and Diop Ousseynou and Akpo Géraud Léra Kelvin and Badji Nfally and Mbaye Gora and Diouf Augustin Louis Diaga and Sy Pape Mady and Djiboune Alphonse and Fashinan Herbert and Farssi Mohamed and Ndoye Oumar and Diarra Mounibé and Mbodji Mamadou},
  title = {Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image},
  journal = {International Journal of Medical Imaging},
  volume = {9},
  number = {4},
  pages = {189-192},
  doi = {10.11648/j.ijmi.20210904.14},
  url = {https://doi.org/10.11648/j.ijmi.20210904.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20210904.14},
  abstract = {Objective: The aim of this study was to describe the stages of learning Kohonen's self-organizing maps applied to scintigraphy imaging in order to perform classification for medical diagnostic aid. Method: To achieve these goals, the neurons, arranged on a regular grid, are connected to each other by a neighbor relationship, which creates the topology of the map. The input layer consisted of pixels from the scintigraphy images. Results: During the iteration rounds of learning, we have seen a deployment of neurons on the nodes of the map that becomes more and more important. And it is the same for the winning neurons. After 750 iterations, the Davies Bouldin index attests to the end of the training with a quantization error that goes from 0.175 at the beginning of the training to 0.0225 at the end of the training. After this study, we find that neurons 41, 62, 121, 101 and 145 have captured most of the data with a peak uptake achieved by neuron 41 which has captured 1048 data. This individualizes the class of high intensities undoubtedly corresponding to metastatic hyperfixations. Conclusion: This innovative method could undoubtedly be integrated into the link in the chain highlighting periarticular metastases in developing countries, most of which do not have a SPECT-CT.},
 year = {2021}
}

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TY  - JOUR
T1  - Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image
AU  - Ndong Boucar
AU  - Djigo Mamoudou Salif
AU  - Mboup Mamadou Lamine
AU  - Tall Khaly
AU  - Bathily El Hadji Amadou Lamine
AU  - Diop Ousseynou
AU  - Akpo Géraud Léra Kelvin
AU  - Badji Nfally
AU  - Mbaye Gora
AU  - Diouf Augustin Louis Diaga
AU  - Sy Pape Mady
AU  - Djiboune Alphonse
AU  - Fashinan Herbert
AU  - Farssi Mohamed
AU  - Ndoye Oumar
AU  - Diarra Mounibé
AU  - Mbodji Mamadou
Y1  - 2021/11/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmi.20210904.14
DO  - 10.11648/j.ijmi.20210904.14
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 189
EP  - 192
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20210904.14
AB  - Objective: The aim of this study was to describe the stages of learning Kohonen's self-organizing maps applied to scintigraphy imaging in order to perform classification for medical diagnostic aid. Method: To achieve these goals, the neurons, arranged on a regular grid, are connected to each other by a neighbor relationship, which creates the topology of the map. The input layer consisted of pixels from the scintigraphy images. Results: During the iteration rounds of learning, we have seen a deployment of neurons on the nodes of the map that becomes more and more important. And it is the same for the winning neurons. After 750 iterations, the Davies Bouldin index attests to the end of the training with a quantization error that goes from 0.175 at the beginning of the training to 0.0225 at the end of the training. After this study, we find that neurons 41, 62, 121, 101 and 145 have captured most of the data with a peak uptake achieved by neuron 41 which has captured 1048 data. This individualizes the class of high intensities undoubtedly corresponding to metastatic hyperfixations. Conclusion: This innovative method could undoubtedly be integrated into the link in the chain highlighting periarticular metastases in developing countries, most of which do not have a SPECT-CT.
VL  - 9
IS  - 4
ER  -

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Author Information

Ndong Boucar

Nuclear Medicine Department of Dalal Jamm Hospital, Cheikh Anta Diop University, Dakar, Senegal
Djigo Mamoudou Salif

Nuclear Medicine Department of Dalal Jamm Hospital, Cheikh Anta Diop University, Dakar, Senegal
Mboup Mamadou Lamine

Bio-informatics Laboratory ESP, Cheikh Anta Diop University, Dakar, Senegal
Tall Khaly

Bio-informatics Laboratory ESP, Cheikh Anta Diop University, Dakar, Senegal
Bathily El Hadji Amadou Lamine

Idrissa Pouye General Hospital of Grand Yoff, Cheikh Anta Diop University, Dakar, Senegal
Diop Ousseynou

Nuclear Medicine Department of Dalal Jamm Hospital, Cheikh Anta Diop University, Dakar, Senegal
Akpo Géraud Léra Kelvin

Radiology Hospital Aristide Le Dantec, Cheikh Anta Diop University, Dakar, Senegal
Badji Nfally

Radiology Hospital Aristide Le Dantec, Cheikh Anta Diop University, Dakar, Senegal
Mbaye Gora

Pharmaceutical Biophysics Department, Cheikh Anta Diop University, Dakar, Senegal
Diouf Augustin Louis Diaga

Pharmaceutical Biophysics Department, Cheikh Anta Diop University, Dakar, Senegal
Sy Pape Mady

Pharmaceutical Biophysics Department, Cheikh Anta Diop University, Dakar, Senegal
Djiboune Alphonse

Pharmaceutical Biophysics Department, Cheikh Anta Diop University, Dakar, Senegal
Fashinan Herbert

Nuclear Medicine Department of Dalal Jamm Hospital, Cheikh Anta Diop University, Dakar, Senegal
Farssi Mohamed

Bio-informatics Laboratory ESP, Cheikh Anta Diop University, Dakar, Senegal
Ndoye Oumar

Idrissa Pouye General Hospital of Grand Yoff, Cheikh Anta Diop University, Dakar, Senegal
Diarra Mounibé

Idrissa Pouye General Hospital of Grand Yoff, Cheikh Anta Diop University, Dakar, Senegal
Mbodji Mamadou

Idrissa Pouye General Hospital of Grand Yoff, Cheikh Anta Diop University, Dakar, Senegal

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APA Style

Ndong Boucar, Djigo Mamoudou Salif, Mboup Mamadou Lamine, Tall Khaly, Bathily El Hadji Amadou Lamine, et al. (2021). Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image. International Journal of Medical Imaging, 9(4), 189-192. https://doi.org/10.11648/j.ijmi.20210904.14

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ACS Style

Ndong Boucar; Djigo Mamoudou Salif; Mboup Mamadou Lamine; Tall Khaly; Bathily El Hadji Amadou Lamine, et al. Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image. Int. J. Med. Imaging 2021, 9(4), 189-192. doi: 10.11648/j.ijmi.20210904.14

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AMA Style

Ndong Boucar, Djigo Mamoudou Salif, Mboup Mamadou Lamine, Tall Khaly, Bathily El Hadji Amadou Lamine, et al. Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image. Int J Med Imaging. 2021;9(4):189-192. doi: 10.11648/j.ijmi.20210904.14

Copy | Download

@article{10.11648/j.ijmi.20210904.14,
  author = {Ndong Boucar and Djigo Mamoudou Salif and Mboup Mamadou Lamine and Tall Khaly and Bathily El Hadji Amadou Lamine and Diop Ousseynou and Akpo Géraud Léra Kelvin and Badji Nfally and Mbaye Gora and Diouf Augustin Louis Diaga and Sy Pape Mady and Djiboune Alphonse and Fashinan Herbert and Farssi Mohamed and Ndoye Oumar and Diarra Mounibé and Mbodji Mamadou},
  title = {Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image},
  journal = {International Journal of Medical Imaging},
  volume = {9},
  number = {4},
  pages = {189-192},
  doi = {10.11648/j.ijmi.20210904.14},
  url = {https://doi.org/10.11648/j.ijmi.20210904.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20210904.14},
  abstract = {Objective: The aim of this study was to describe the stages of learning Kohonen's self-organizing maps applied to scintigraphy imaging in order to perform classification for medical diagnostic aid. Method: To achieve these goals, the neurons, arranged on a regular grid, are connected to each other by a neighbor relationship, which creates the topology of the map. The input layer consisted of pixels from the scintigraphy images. Results: During the iteration rounds of learning, we have seen a deployment of neurons on the nodes of the map that becomes more and more important. And it is the same for the winning neurons. After 750 iterations, the Davies Bouldin index attests to the end of the training with a quantization error that goes from 0.175 at the beginning of the training to 0.0225 at the end of the training. After this study, we find that neurons 41, 62, 121, 101 and 145 have captured most of the data with a peak uptake achieved by neuron 41 which has captured 1048 data. This individualizes the class of high intensities undoubtedly corresponding to metastatic hyperfixations. Conclusion: This innovative method could undoubtedly be integrated into the link in the chain highlighting periarticular metastases in developing countries, most of which do not have a SPECT-CT.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Neural Method Based on Kohonen Topological Maps Applied to the Whole-body Scintigraphy Image
AU  - Ndong Boucar
AU  - Djigo Mamoudou Salif
AU  - Mboup Mamadou Lamine
AU  - Tall Khaly
AU  - Bathily El Hadji Amadou Lamine
AU  - Diop Ousseynou
AU  - Akpo Géraud Léra Kelvin
AU  - Badji Nfally
AU  - Mbaye Gora
AU  - Diouf Augustin Louis Diaga
AU  - Sy Pape Mady
AU  - Djiboune Alphonse
AU  - Fashinan Herbert
AU  - Farssi Mohamed
AU  - Ndoye Oumar
AU  - Diarra Mounibé
AU  - Mbodji Mamadou
Y1  - 2021/11/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmi.20210904.14
DO  - 10.11648/j.ijmi.20210904.14
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 189
EP  - 192
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20210904.14
AB  - Objective: The aim of this study was to describe the stages of learning Kohonen's self-organizing maps applied to scintigraphy imaging in order to perform classification for medical diagnostic aid. Method: To achieve these goals, the neurons, arranged on a regular grid, are connected to each other by a neighbor relationship, which creates the topology of the map. The input layer consisted of pixels from the scintigraphy images. Results: During the iteration rounds of learning, we have seen a deployment of neurons on the nodes of the map that becomes more and more important. And it is the same for the winning neurons. After 750 iterations, the Davies Bouldin index attests to the end of the training with a quantization error that goes from 0.175 at the beginning of the training to 0.0225 at the end of the training. After this study, we find that neurons 41, 62, 121, 101 and 145 have captured most of the data with a peak uptake achieved by neuron 41 which has captured 1048 data. This individualizes the class of high intensities undoubtedly corresponding to metastatic hyperfixations. Conclusion: This innovative method could undoubtedly be integrated into the link in the chain highlighting periarticular metastases in developing countries, most of which do not have a SPECT-CT.
VL  - 9
IS  - 4
ER  -

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