Analysis of Deep Learning and Machine Learning Methods for Breast Cancer Detection

Breast cancer is the disease with the greatest incidence rate and the fastest global spread. Compared to men, women are diagnosed with breast cancer far more frequently. If detected early enough, breast cancer can be successfully treated. One of the leading causes of death internationally, breast cancer accounted for 22.6% of all cancer-related deaths worldwide in 2013. This indicates that more than one crore cancer-related deaths were brought on by breast cancer. 14.7% of all cancer cases in India are caused by breast cancer. Early breast cancer recognition has been the subject of extensive investigation since it can help with treatment start-up and reduce overall mortality rates. Many instances can be managed and the overall death rate can be lowered with an early diagnosis. Breast cancer has been the subject of numerous research studies. Machine learning is the method being applied in research. Older machine learning-based research that excelled in their respective fields include those that used decision trees, KNN, SVM, and naive bays techniques. Better algorithms have been created as a result of these investigations. On the other hand, deep learning, a recently created technique, is currently being utilised to categorize breast cancer. Data science commonly makes use of convolutional neural networks, recurrent neural networks, and other deep learning-based techniques. Deep learning algorithms outperform machine learning algorithms in terms of performance. The most captivating portions of the photographs have been removed. Many academics utilize CNN to categorize the images. In a nutshell, CNN is the technique that is most frequently used to categorize images. This piece of work compares and contrasts the employment of machine learning and deep learning techniques in numerous journals.