Breast Cancer Diagnosis Using Histopathology and Convolution Neural Network CNN Method

Abstract

High-performance computer tools have been more widely available, and deep learning systems that utilize deep neural networks have become increasingly common in many fields. Deep learning approaches based on convolution neural networks (CNN) have become more widespread as high-performance computer facilities have grown. An overview of the growth of deep learning models and a concise explanation of various learning approaches, such as supervised learning, trains the neural network using labeled data. Solid experiments are required in medical image analysis studies to prove the efficacy of proposed approaches. Many architectures, such as Pre-trained Networks and Convolution Neural Networks CNN, are employed to achieve breast cancer diagnosis. Various classification measures may be utilized, making comparison of the methodologies challenging. Medical screening methods have grown increasingly important in the detection and treatment of diseases. Early identification of breast cancer is regarded to be a crucial element in lowering women's mortality rates. Several different breast screening modalities are being investigated to improve breast cancer diagnosis. Histopathology is used in a current cancer detection and localization method that uses artificial intelligence to screen for breast cancer and identify the existence of tumors in the breast. This study focused on an experimental dataset that employed convolution neural network (CNN) techniques to detect and localize breast tumors (i.e., pre-trained CNN). CNNs are a powerful tool for solving real-world problems, and neural networks with learning algorithms are a promising new technology.