Early Breast Cancer Detection Using CNN and RNN

Resumen

Detecting breast cancer early provides patients with superior opportunities to receive effective treatment and enhance their life expectancy. Various deep learning techniques are used together in this study to develop a CNN-RNN hybrid system that optimizes breast cancer detection at an early stage. Medical images then enter the CNN module which analyzes spatial features of mammograms as well as histological data based on tumor patterns indicative of malignant or benign conditions. The extracted data features flow into LSTM networks as part of RNNs to process temporal sequences and advance classification accuracy. The integrated approach exaggerates the core advantages of each architecture to establish robustness when processing intricate medical imaging data. The model shows excellent results on benchmark tests which indicate its potential to assist radiologists through reliable early breast cancer diagnosis