Predicting invasive ductal carcinoma using a Reinforcement Sample Learning Strategy using Deep Learning

TL;DR

This paper introduces a novel deep learning-based method using convolutional neural networks and reinforcement learning to improve automated detection and classification of invasive ductal carcinoma in mammogram images, aiding radiologists.

Contribution

It proposes a new tumor classification algorithm that enhances feature extraction and training speed in mammogram analysis using CNNs and reinforcement learning strategies.

Findings

Improved accuracy in detecting invasive ductal carcinoma.

Enhanced feature extraction from mammogram images.

Faster training process for the classification algorithm.

Abstract

Invasive ductal carcinoma is a prevalent, potentially deadly disease associated with a high rate of morbidity and mortality. Its malignancy is the second leading cause of death from cancer in women. The mammogram is an extremely useful resource for mass detection and invasive ductal carcinoma diagnosis. We are proposing a method for Invasive ductal carcinoma that will use convolutional neural networks (CNN) on mammograms to assist radiologists in diagnosing the disease. Due to the varying image clarity and structure of certain mammograms, it is difficult to observe major cancer characteristics such as microcalcification and mass, and it is often difficult to interpret and diagnose these attributes. The aim of this study is to establish a novel method for fully automated feature extraction and classification in invasive ductal carcinoma computer-aided diagnosis (CAD) systems. This article presents a tumor classification algorithm that makes novel use of convolutional neural networks on breast mammogram images to increase feature extraction and training speed. The algorithm makes two contributions.