Learning Curve Analysis on Adam, Sgd, and Adagrad Optimizers on a Convolutional Neural Network Model for Cancer Cells Recognition

Abstract

Is early cancer detection using deep learning models reliable? The creation of expert systems based on Deep Learning can become an asset for the achievement of an early detection, offering a preliminary diagnosis or a second opinion, as if it were a second specialist, thus helping to reduce the mortality rate of cancer patients. In this work, we study the differences and impact of various optimizers and hyperparameters in a Convolutional Neural Network model, to then be tested on different datasets. The results of the tests are analyzed and an implementation of a cancer classification model is proposed focusing on the different approaches of the selected Optimizers as the best method for the achievement of optimal results in accurately improving the detection of cancerous cells. Cancer, despite being considered one of the biggest health problems worldwide, continues to be a major problem because its cause remains unknown. Regular medical check-ups are not frequent in countries where access to specialized health services is not affordable or easily accessible, leading to detection in more advanced stages when the symptoms are quite visible. To reduce cases and mortality rates ensuring early detection is paramount.