A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications

Brijesh Verma; Rinku Panchal; Kuldeep Kumar

doi:10.1109/IJCNN.2003.1223720

A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications

Brijesh Verma^*, Rinku Panchal, Kuldeep Kumar

^*Corresponding author for this work

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

4 Citations (Scopus)

Abstract

The paper proposes a novel min-max feature value based neural architecture and learning algorithm for classification of microcalcification patterns in digital mammograms. The neural architecture has a single hidden layer and it has a fixed number of hidden units and outputs. One class is represented by three hidden units and an output. The suspicious areas represented by chain code, are extracted from digital mammograms. The feature values are extracted for benign and malignant microcalcifications. A set of min, average and max values for every input feature is defined and assigned to the weights between input and hidden layer. The weights of the output layer are calculated using least squares methods or assigned in such a way that it maximizes the output value for only one class. Many experiments were conducted on a benchmark database of digital mammograms and comparative results are included in this paper.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Place of Publication	Portland
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	2033-2038
Number of pages	6
Volume	3
ISBN (Print)	0-7803-7898-9
DOIs	https://doi.org/10.1109/IJCNN.2003.1223720
Publication status	Published - 2003
Event	International Joint Conference on Neural Networks 2003 - Doubletree Hotel - Jantzen Beach, Portland, OR, United States Duration: 20 Jul 2003 → 24 Jul 2003 http://www.conference123.org/ijcnn2003/

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks, 2003
Publisher	IEEE
ISSN (Print)	1098-7576

Conference

Conference	International Joint Conference on Neural Networks 2003
Abbreviated title	IEEE
Country/Territory	United States
City	Portland, OR
Period	20/07/03 → 24/07/03
Internet address	http://www.conference123.org/ijcnn2003/

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10.1109/IJCNN.2003.1223720

Cite this

Verma, B., Panchal, R., & Kumar, K. (2003). A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. In Proceedings of the International Joint Conference on Neural Networks (Vol. 3, pp. 2033-2038). (Proceedings of the International Joint Conference on Neural Networks, 2003). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2003.1223720

Verma, Brijesh ; Panchal, Rinku ; Kumar, Kuldeep. / A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. Proceedings of the International Joint Conference on Neural Networks. Vol. 3 Portland : IEEE, Institute of Electrical and Electronics Engineers, 2003. pp. 2033-2038 (Proceedings of the International Joint Conference on Neural Networks, 2003).

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title = "A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications",

abstract = "The paper proposes a novel min-max feature value based neural architecture and learning algorithm for classification of microcalcification patterns in digital mammograms. The neural architecture has a single hidden layer and it has a fixed number of hidden units and outputs. One class is represented by three hidden units and an output. The suspicious areas represented by chain code, are extracted from digital mammograms. The feature values are extracted for benign and malignant microcalcifications. A set of min, average and max values for every input feature is defined and assigned to the weights between input and hidden layer. The weights of the output layer are calculated using least squares methods or assigned in such a way that it maximizes the output value for only one class. Many experiments were conducted on a benchmark database of digital mammograms and comparative results are included in this paper.",

author = "Brijesh Verma and Rinku Panchal and Kuldeep Kumar",

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booktitle = "Proceedings of the International Joint Conference on Neural Networks",

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note = "International Joint Conference on Neural Networks 2003, IEEE ; Conference date: 20-07-2003 Through 24-07-2003",

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Verma, B, Panchal, R & Kumar, K 2003, A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. in Proceedings of the International Joint Conference on Neural Networks. vol. 3, Proceedings of the International Joint Conference on Neural Networks, 2003, IEEE, Institute of Electrical and Electronics Engineers, Portland, pp. 2033-2038, International Joint Conference on Neural Networks 2003, Portland, OR, United States, 20/07/03. https://doi.org/10.1109/IJCNN.2003.1223720

A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. / Verma, Brijesh; Panchal, Rinku; Kumar, Kuldeep.
Proceedings of the International Joint Conference on Neural Networks. Vol. 3 Portland: IEEE, Institute of Electrical and Electronics Engineers, 2003. p. 2033-2038 (Proceedings of the International Joint Conference on Neural Networks, 2003).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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AB - The paper proposes a novel min-max feature value based neural architecture and learning algorithm for classification of microcalcification patterns in digital mammograms. The neural architecture has a single hidden layer and it has a fixed number of hidden units and outputs. One class is represented by three hidden units and an output. The suspicious areas represented by chain code, are extracted from digital mammograms. The feature values are extracted for benign and malignant microcalcifications. A set of min, average and max values for every input feature is defined and assigned to the weights between input and hidden layer. The weights of the output layer are calculated using least squares methods or assigned in such a way that it maximizes the output value for only one class. Many experiments were conducted on a benchmark database of digital mammograms and comparative results are included in this paper.

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Verma B, Panchal R, Kumar K. A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications. In Proceedings of the International Joint Conference on Neural Networks. Vol. 3. Portland: IEEE, Institute of Electrical and Electronics Engineers. 2003. p. 2033-2038. (Proceedings of the International Joint Conference on Neural Networks, 2003). doi: 10.1109/IJCNN.2003.1223720

A novel min-max feature value based neural architecture and learning algorithm for classification of microcalcifications

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