Breast cancer detection using machine learning with thermograms in an edge computing scenario
| Autor: | Maryam Tahmooresi, Jesus Alcober, David Remondo |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Telemàtica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica, Universitat Politècnica de Catalunya. BAMPLA - Disseny i Avaluació de Xarxes i Serveis de Banda Ampla |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Enginyeria de la telecomunicació::Aspectes socials [Àrees temàtiques de la UPC]
business.industry Computer science Cancer Machine learning computer.software_genre medicine.disease Computing methodologies Telecomunicació de banda ampla Sistemes de Machine learning approaches Breast cancer Computational neuroscience medicine Artificial intelligence business computer Broadband communication systems Neurociència computacional Edge computing Neural networks Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions [Àrees temàtiques de la UPC] |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| DOI: | 10.1145/3469258.3469850 |
| Popis: | The second cause of death in the world is cancer. Although breast cancer is the more common cause of death among women, the chance of survival can be increased by detecting cancer in the early stages. For this aim, there are different tests such as Magnetic Resonance Imaging (MRI), mammogram, ultrasound, thermogram and among these tests, the mammogram is the one which is used more frequently. Regarding the advantages and the results, which are achieved by thermogram, it can be a good alternative or complement for the mammogram if we can improve the weaknesses of the thermogram. For this reason, in this research, we work on a thermogram to see the possibility of having a good performance. On the other hand, we train another model by sending personal patients' information to see the effect of these data to improve the performance of breast cancer detection. In the end, we plan to separate the process between edge and core host to do the process faster, safer, and cost-effective. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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