Deep Learning and Machine Learning Approach to Breast Cancer Classification with Random Search Hyperparameter Tuning - Kawina, Inutu; K. , Amarendra; Marapelli , Bhaskar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Breast cancer is one of the main causes of death and a threat to women, artificial intelligence has grown in importance in the field of health over time. With the aid of random search hyperparameter tuning, this study proposed a machine learning and deep learning approach to breast cancer classification. Two datasets related to breast cancer were used in this study, and findings proved that optimizing random search hyperparameters enhances the models performance, in addition it is seen that machine learning classifiers are not as effective in classifying breast cancer as compared to deep learning. Among the deep learning approaches Convolutional neural networks showed the highest accuracy over deep neural networks on both datasets. It was further observed that random search hyperparameter tuning performed better on the Breakhis_400x dataset than on the breast histopathology dataset which could be attributed to the notion that hyperparameter tuning using random search performs better on small data than large data.