Work

Towards Early Breast Cancer Detection: A Deep Learning Approach

Year: 2024

Type: article

Abstract: Early detection of breast cancer is crucial for patients' recovery chances to be improved. Artificial intelligence techniques, and more particularly Deep Learning (DL), may contribute to enhancing the accuracy of this detection. The main objective of this paper is to propose a DL model in an attempt to detect and classify breast cancer, and thus help people suffering from this disease. The Breast Cancer Wisconsin dataset was implemented to train neural networks, and their performance was subsequently evaluated on certain test datasets. The findings revealed that this approach provides promising results in terms of detection accuracy, with high sensitivity and specificity. The study also compares the performance of this approach with other breast cancer detection works, demonstrating that DL can provide significantly better results.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.8634

Authors: Amina Bekkouche, Mohammed Merzoug, Mourad Hadjila, Wafaa Ferhi

Institution: University of Abou Bekr Belkaïd

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $431