Improvisation of Breast Cancer Detection using LSTM Algorithm

DOI

10.2991/978-94-6463-250-7_31How to use a DOI?

Keywords

Deep Learning; RNN; LSTM; Mammogram; Recurrent Neural Network; Long Shot-term Memory; Breast Cancer Detection

Abstract

The impact of AI on healthcare is growing every day. A great deal of work is being done to improve the effectiveness of cancer diagnosis in its early stages, where medical imaging is essential. Breast cancer is alarmingly on the rise among women, with an Indian woman receiving a breast cancer diagnosis every four minutes. Even though breast cancer is treatable, early detection of the condition is crucial to a positive prognosis. Mammograms were once the only method for identifying breast cancer. Mammography, however, is ineffective for women of all ages, leading to an excessive number of false positive and false negative cases. This has severe effects. This experimental paper describes utilizing an LSTM (Long Short-Term Memory Network),deep learning technique to get around this constraint. It is an advanced recurrent neural network, that can solve the vanishing gradient issue, that the recurrent neural network encounters when attempting to identify the malignant region in mammograms. After receiving approval from the scientific and ethical committee, this experiment was carried out using the Real Time Data set of mammograms obtained from a hospital with 1250 beds. In this experimental study, a total of 1646 mammogram images from 414 patients were used. Among the real-time mammography data set, the LSTM algorithm provided a detection accuracy of more than 90% for the signs of breast cancer, by providing a relevant Bi-RADS score.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Ruban
AU  - Mohamed Moosa Jabeer
AU  - Ram Shenoy Basti
PY  - 2023
DA  - 2023/10/17
TI  - Improvisation of Breast Cancer Detection using LSTM Algorithm
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 178
EP  - 186
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_31
DO  - 10.2991/978-94-6463-250-7_31
ID  - Ruban2023
ER  -