ItemArticle

LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm

Title
LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm
Creator
Umamaheswari, D.; Kannan, M.; Mary, I. Priya Stella; Rozario, D. Juliet; Savitha, P. Margaret; Manimekala, B.
Description
One of the most prevalent and severe tumors in women, breast cancer, remains a major global health issue despite a notable increase in incidence over the last ten years. It is the second leading cause of cancer-related death among women. Identifying breast cancer in its early stages has the potential to save lives; however, current screening techniques for the illness require several laboratory procedures involving medical experts. Automated solutions with rapid and reliable diagnostic capabilities are needed to minimize human error and expedite breast cancer diagnosis. The projected accuracy of cancer diagnosis remains far from matching the precision offered by existing approaches, even with the research on automated systems for the disease being studied. This work suggests a long short-term memory-based modified Gorilla troop optimization (LSTM-MGTO) method for breast cancer classification in order to address these issues. The Mastectomy Koibra Dataset (BCCD) and Wisconsin Diagnostic Mastectomy (WDBC) datasets were used to test the suggested methods. First, the proposed system employs contrast-limited adaptive histogram equalization (CLAHE) to enhance the quality of digital mammograms. Furthermore, employ a semantic deep learning (SDL) model to extract features. After the feature selection process, a recursive feature elimination technique was implemented to determine the crucial WDBC and BCCD characteristics that are relevant to breast cancer detection. Moreover, recommend a modified U-Net architecture for partitioning in both unmapped and guided contexts. The experimental findings indicate that the newly developed partitioning model surpasses existing advanced techniques, yielding superior results in both Dice and IoU score evaluations. On the WDBC and BCCD datasets, the suggested U-Net segmentation produces maximum Dice scores of 97.65% and 96.24%, respectively. Additionally, the model obtained the greatest IoU scores of 95.43% and 90.65% on the WDBC and BCCD datasets, respectively, according to the experimental findings. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025.
Source
Network Modeling Analysis in Health Informatics and Bioinformatics;Volume;14;Issue;1;Article No.;17;
Date
01-01-2025
Publisher
Springer
Subject
Intersection over Union (IoU) metric; LSTM-MGTO (a technique combining long short-term memory and modified Gorilla troop optimization); Mammary neoplasm; U-Net (a neural network architecture); WDBC and BCCD (two datasets related to breast cancer diagnosis)
Coverage
Umamaheswari D., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Kannan M., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Mary I.P.S., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Rozario D.J., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Savitha P.M., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Manimekala B., Department of Computer Science, Christ University, Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 21926662;
Format
online
Language
English
Type
Article

Citation

Umamaheswari, D.; Kannan, M.; Mary, I. Priya Stella; Rozario, D. Juliet; Savitha, P. Margaret; Manimekala, B., “LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/22052.

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