Deep Learning Optimizers for Histological Image Classification: A Comparative Study

Over the last few years, there has been a great deal of progress in the field of image recognition, using classical or modern methods based on machine learning algorithms. In this context, numerous studies on object detection have been carried out in various fields, with promising results. The aim of this paper is the design of an automatic classification system for tumor tissue cells (breast cancer) in medical images (histological images). The proposed approach relies on the use of convolutional neural networks (CNNs), with numerous experiments using different optimization algorithms such as RMSprop, SGD or Adam to evaluate the performance of our approach. The findings show the effectiveness of the RMSprop optimizer, with a fast convergence rate and high accuracy.