Deep learning untuk pendeteksian penyakit kanker payudara dengan optimasi Adam - Irmawati Irmawati; Yuris Alkhalifi; Agung Fazriansyah; Mohammad Syamsul Azis; Kudiantoro Widianto (JISAMAR (Journal of Information System, Applied, Management, Accounting and Research), 2023)
Breast cancer is the second leading cause of death in female patients in the world. Breast cancer has caused death in more than 100 countries. Early diagnosis of breast cancer patients is important to reduce the possibility of death. Researchers focus on accurate breast cancer detection, automated diagnostic methods and breast cancer diagnosis. This paper proposes Adam's optimization for Deep Learning Algorithm to classify breast cancer detection. This study aims to overcome the problem of data instability and overfitting, as well as update network weights on deep learning training data. In this study, the authors conducted experiments with a combination of three hidden layers and learning speed to improve classification accuracy. The experiment used the breast cancer data set obtained from the UCI Study: the WBCD data set (Original) while the experimental results showed that the proposed scheme achieved 96.3% accuracy for classifying breast cancer.