An Approach For Cervical and Breast Cancer Classification Using Deep Learning: A Comprehensive Survey

Abstract

The most common malignancies in women worldwide are breast & cervical, but very few researchers have examined how gender expectations affect diagnostic adherence provided sexual implications and physical contact. Considering these perceptions is essential to enhancing diagnostic accuracy and services since they may be a major factor in decision-making. As cervical and breast treatments for cancer to be effective, accurate early detection is essential. Machine learning and deep learning are being used by an expanding population and businesses to analyze vast volumes of data and provide useful insights. It has become quite frequent in clinical practices to use ML-based techniques to predict the initial stages of major illnesses like cancer, renal failure, and cardiovascular diseases. Several of the most prevalent diseases in women include cervical cancer, and early detection could help reduce mortality and morbidity. The paper provides a comprehensive analysis of the techniques and research issues widely employed now in the area. The causes, as well as mortality statistics of cancer, are also covered in this. The goal of the paper is to present a deep learning-centric system for earlier and more accurate breast and cervical cancer prediction. The Convolutional neural networks (CNN) were utilized in the progress of deep learning models. This research analyzes the effectiveness of the CNN model employing transfer learning with a model that has already been trained (VGG16). Considering the results, deep learning algorithms have the capacity to anticipate disease somewhere at the earliest stage at which fatality rates can be reduced.