EarlyNet: A Novel Transfer Learning Approach with VGG11 and Efficient Net for Early-Stage Breast Cancer Detection - Souza, Melwin D.; Prabhu G., Ananth; Kumara, Varuna; Prameela, Prameela; K. M., Chaithra (International Journal of Intelligent Systems and Applications in Engineering, 2024)
One of the malignancies that affect women the most frequently is breast cancer. An early diagnosis of this malignancy is crucial for therapeutic and epidemiologic purposes since it helps to inform future therapy. The amount of females who are detected with breast cancer keeps rising, particularly in proportion to the growing elderly population. Mammography screening procedures need to be improved so that they are more effective and don’t waste as much time. In the case of technological development, there is never a shortage of opportunities in the field of medical imaging. Cancer patients who have an earlier diagnosis of their disease have a lower probability of passing away from their illness. This research proposed an novel early neural network based on transfer learning names as ‘EARLYNET’ to automate breast cancer prediction. In this research, the new hybrid deep learning model was devised and built for distinguishing benign breast tumors from malignant ones. The trials were carried out on the Breast Histopathology Image dataset, and the model was evaluated using a Mobilenet founded on the transfer learning method. In terms of accuracy, this model delivers 89.53% accuracy.