ABSTRACT
Breast cancer is a disease characterized by the rapid and uncontrolled growth of cells in the breast. It occurs when a malignant (cancerous) tumor originates in the breast cells. Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall, with around 2 million cases observed in 2018. Early diagnosis of breast cancer can significantly improve prognosis and survival rates by enabling timely clinical treatment. Accurate classification of benign tumors can also prevent patients from undergoing unnecessary treatments. Thus, the correct diagnosis of breast cancer and the classification of patients into malignant or benign groups is the focus of much research. Due to its unique advantages in detecting critical features from complex breast cancer datasets, machine learning (ML) is widely recognized as the methodology of choice in breast cancer pattern classification. This project involves a study on the implementation of a Convolutional Neural Network (CNN) model using a dataset from the UCI repository. We have obtained an accuracy of 93% with the CNN model.

OBJECTIVE
The primary objective of this project is to develop and evaluate a Convolutional Neural Network (CNN) model for the early prediction of breast cancer using data obtained from Fine Needle Aspiration (FNA) procedures. By leveraging advanced machine learning techniques, particularly CNNs, we aim to automate the diagnostic process, thereby reducing the need for multiple diagnostic tests such as mammography, ultrasound, and biopsies. This automation not only minimizes patient discomfort but also decreases their exposure to radiation. Additionally, we seek to enhance the accuracy of breast cancer diagnosis by utilizing the CNN model to decrease the rates of false positive and false negative diagnoses. To validate the effectiveness of our approach, we will benchmark the performance of the CNN model against traditional classification methods, including Decision Tree, K-Neighbors, Logistic Regression, Random Forest, and Support Vector Machine, using the Wisconsin Breast Cancer (original) Datasets. Through this comprehensive evaluation, we aim to contribute to the development of more efficient and accurate breast cancer diagnostic tools, ultimately improving patient care and outcomes.