AI-Powered Detection: Implementing Deep Learning for Breast Cancer Prediction
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 3
Abstract
Breast cancer remains a critical global health issue, affecting millions of women worldwide. According to the World Health Organization (WHO), there were 2.3 million new cases and 685,000 deaths from breast cancer in 2020 alone. This makes breast cancer the most prevalent cancer globally, with 7.8 million cases diagnosed over the past five years. As the prevalence of breast cancer continues to rise, the need for accurate and efficient diagnostic tools becomes increasingly urgent. Artificial Intelligence (AI) has shown considerable promise in enhancing breast cancer detection and diagnosis. Over the past two decades, AI tools have increasingly aided physicians in interpreting mammograms, offering the potential for automated, precise, and early cancer detection. However, significant challenges remain, particularly concerning data imbalance in datasets—where cancerous images are often underrepresented—and the issue of low pixel resolution, which can obscure crucial details in medical images. This work utilizes a subset of the data called Mini-DDSM, a lightweight version of the Digital Database for Screening Mammography. To address these challenges, our research employed the Neighborhood Cleaning Rule (NCR) algorithm from the imbalance library, designed to mitigate data imbalance by refining the dataset through the selective removal of noisy and borderline examples. This method enhances the quality of training data, enabling AI models to learn more effectively. We developed a deep learning model that incorporates a transfer learning layer (DenseNet121), dense layers, a global pooling layer, and a dropout layer to optimize performance. This model demonstrated promising results, effectively addressing the challenges of data imbalance and low image resolution. Our approach underscores the potential of AI to significantly improve breast cancer detection and diagnosis, ultimately leading to better patient outcomes. Continued research and refinement of AI techniques will be crucial in overcoming remaining challenges and fully realizing the potential of these technologies in healthcare.
Authors and Affiliations
Anmol Tanveer, Saima Munawar, Nasir Naveed
Keywords
Related Articles
- EP ID EP760442
- DOI -
- Views 7
- Downloads 0
AlzheimerNet-V3: Automated Deep Learning Approach for Detecting Alzheimer's Disease
Introduction/Importance of Study: Alzheimer’s Disease (AD) stands as the highly prevalent form of dementia, culminating in a progressive neurological brain disorder characterized by deteriorating memory function and i...
Osteochondroma Identification Through Transfer Learning and Convolutional Neural Networks
Accurate and timely diagnosis of musculoskeletal conditions like osteochondroma is pivotal in ensuring effective treatment and improved patient outcomes. However, traditional diagnostic methods relying on manual interp...
AI-Driven Weed Classification for Improved Cotton Farming in Sindh, Pakistan
This research study proclaims the combination of artificial intelligence and also IoT in precision agriculture, highlighting weed discovery plus cotton plant monitoring in Sindh, Pakistan. The uniqueness lies in creati...
Impact of Internal Forces on Employee Behaviors: Role of Situational Factors
The current research investigated the effects of motivation, ability, and role perception (internal forces), also known as drivers on employee behaviors as well as to find out the moderating role of situational factors...
Numerical Simulation of Flow Past a Square Object Detached with Controlling Object at Various Reynolds Number
A two-dimensional (2-D) numerical study has been conducted for flow past of two different configurations of square objects by using the numerical technique Lattice Boltzmann Method (LBM). In these configurations, one o...