A Review of Breast Cancer Detection Using Deep Learning Techniques
Abhishek Das, Mihir Narayan Mohanty · 2022
Cancer research is a special space for medical and technological professionals. For a few years, research has grown and maximum involvement of technological research has been visible. In this article, earlier works on detection are analyzed. A model is proposed that performs well and will take readers to the next level of work. Invasive ductal carcinoma detection was studied. Though many types of detection methods have been developed, this review provides deep learning-based detection as a short review. Information was gathered from most of the recent developments in the field of breast cancer detection to provide present aspects as well as future perspectives. Furthermore, breast histopathology images were classified using the long short-term memory model-based approach. The Adam optimization algorithm was used to minimize error, and to train the network that is one of the supervised learning methods. To check the practicability of the proposed method a publicly available breast cancer dataset was used to train, validate, and test the network. The proposed method resulted in 98% accuracy – a better achievement in comparison to the state-of-the-art methods in medical image processing.