An Artificial Intelligence Model for Early Stage Breast Cancer Detection from Biopsy Images

TL;DR

This paper introduces a CNN-based AI tool that accurately classifies breast cancer types from biopsy images, aiming to improve diagnosis speed and reduce invasive testing.

Contribution

The study develops a deep learning model that enhances breast cancer classification accuracy directly from biopsy images, reducing reliance on additional invasive tests.

Findings

Outperforms existing methods in accuracy, precision, recall, and F1-score.

Effectively distinguishes benign from malignant tissues and classifies cancer subtypes.

Demonstrates potential for clinical application in diagnostic workflows.

Abstract

Accurate identification of breast cancer types plays a critical role in guiding treatment decisions and improving patient outcomes. This paper presents an artificial intelligence enabled tool designed to aid in the identification of breast cancer types using histopathological biopsy images. Traditionally additional tests have to be done on women who are detected with breast cancer to find out the types of cancer it is to give the necessary cure. Those tests are not only invasive but also delay the initiation of treatment and increase patient burden. The proposed model utilizes a convolutional neural network (CNN) architecture to distinguish between benign and malignant tissues as well as accurate subclassification of breast cancer types. By preprocessing the images to reduce noise and enhance features, the model achieves reliable levels of classification performance. Experimental results on such datasets demonstrate the model's effectiveness, outperforming several existing solutions in terms of accuracy, precision, recall, and F1-score. The study emphasizes the potential of deep learning techniques in clinical diagnostics and offers a promising tool to assist pathologists in breast cancer classification.