Breast Cancer Classification Based on Mammogram Images Using CNN Method with NASNet Mobile Model - Pramesti, Diah Devi; Farida, Yuniar; Novitasari, Dian Candra Rini; Wibowo, Achmad Teguh (IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., 2025)
In Indonesia, the type of cancer that contributes to the highest death rate is breast cancer, so there is a great need for early examination, clinical examination, and screening, which includes mammography. Mammography is currently the most effective method for detecting early-stage breast cancer. This study aims to classify breast cancer cells based on mammogram images. The method used in this research is CNN (Convolutional Neural Network) with the NASNet Mobile model for classifying three classes: normal, benign, and malignant. The CNN method can learn various input attributes powerfully so that CNN can obtain more detailed data characteristics and has better detection capabilities. This research obtained the most optimal model based on the percentage of accuracy, sensitivity, and specificity values of 99.67%, 98.78%, and 99.35%, respectively. This research can be used to help radiologists as considerations in making breast cancer diagnosis decisions.