Abstract
Breast Cancer (BC) is one among the most critical diseases affecting women across the world mostly between the ages 35-55 and is growing every year in a capricious rate. By identifying the disease in its early stages, we can control the occurrence of BC. The manual approach used by radiologists has failed since they are similar in the appearance of micro-calcifications, and segmentation of breast images are complicated process. Hence there is a need of automated systems for detection of the disease in early-stage which helps to assist radiologists for diagnosing the disease in a precise manner and make necessary decisions for patients' treatment in the future. For classifying and predicting the disease, swarm intelligence (SI) plays a major role in terms of obtaining an optimal solution and reducing its training time. This paper presents study on review and analyses the performance of breast cancer disease predictive modelling using swarm intelligence. Also, it recommends the solution for forthcoming exertion to predict the location of the tumour in an efficient manner.
| Original language | English |
|---|---|
| Title of host publication | 2020 International Conference on Computational Performance Evaluation, ComPE 2020 |
| Editors | Sudip Paul, Jitendra Kumar Verma |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 523-530 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781728166445 |
| ISBN (Print) | 9781728166445 |
| DOIs | |
| Publication status | Published - 2 Jul 2020 |
| Event | 2020 International Conference on Computational Performance Evaluation, ComPE 2020 - Shillong, India Duration: 2 Jul 2020 → 4 Jul 2020 |
Publication series
| Name | 2020 International Conference on Computational Performance Evaluation, ComPE 2020 |
|---|
Conference
| Conference | 2020 International Conference on Computational Performance Evaluation, ComPE 2020 |
|---|---|
| Country/Territory | India |
| City | Shillong |
| Period | 2/07/20 → 4/07/20 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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