Deep Learning Paradigms for Breast Cancer Diagnosis: A Comparative Study on Wisconsin Diagnostic Dataset

Abstract

Breast cancer is a highly common and life-threatening disease that affects people worldwide. Early and accurate diagnosis of breast cancer can enhance patients' prognosis and survival rate. This paper conducts a comparative examination of the Wisconsin Breast Cancer Diagnostic (WBCD) dataset by employing four distinct deep learning models: Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The collection consists of 569 examples of Fine Needle Aspirate (FNA) photographs of breast cancers, with each case containing thirty parameters that define the features of the cell nuclei. By doing a comparative analysis of the advantages and disadvantages of the models, we will evaluate them based on their accuracy, precision, recall, and F1-score. Based on our research, CNN achieves the best level of accuracy at 98.25%, which is followed by GRU at 97.37%, FNN at 96.49%, and LSTM at 95.61%. It is determined that CNN is the most suitable model for this task and that deep learning models are valuable and encouraging tools for diagnosing breast cancer.