Invasive Ductal Carcinoma Prediction in Mammography and Histopathology Images Using CNN
Garima Choudhary, Himanshi Mendiratta, Damandeep Kaur · 2023
This study investigates the application of a deep learning model to the diagnosis of invasive ductal carcinoma (IDC) from mammography and histopathology images. The ultimate goal is to improve early detection and treatment of breast cancer and reduce mortality. The study used the InceptionResNetV2 model for the mammography dataset, which produced an impressive accuracy of 95.84%. A standard Convolutional Neural Network (CNN) model was used for the histopathology dataset, with a ReLU function, 128 neurons, and a Softmax layer with 2 neurons and this model achieved an accuracy of 96.23%. These results demonstrate the ability of deep learning models to accurately predict IDC from medical imaging data, which has the potential to improve patient outcomes by enabling earlier diagnosis and treatment. By emphasizing the effectiveness of these models, this review contributes to the development of more accurate diagnostic tools and breast cancer treatment strategies.