Breast Cancer Image Classification Obtained Through Dynamic Thermography using Deep Learning

This paper addresses the vital task of diagnosing breast cancer early and accurately. It acknowledges the difficulty in detecting minor variations in breast cancer patterns through visual examination alone, particularly when analyzing gray scale images. To overcome this challenge, our research concentrates on using image processing models, particularly Convolutional Neural Networks (CNNs), and enhances them with a refined approach to classification. The research methodology encompasses rigorous data collection, preprocessing, and strategic feature selection to enhance model robustness. To address challenges related to data intricacies, the study incorporates transfer learning, refining the model’s training process for improved accuracy. The paper not only seeks to elevate diagnostic precision and minimize errors but also aims to provide healthcare professionals with efficient tools for breast cancer detection. Building upon prior research, the study underscores the potential of deep learning methodologies in advancing breast cancer diagnosis. Furthermore, the fine-tuning process involves adapting a pre-trained model to the intricacies of breast cancer patterns, optimizing its performance. The ultimate goal is to contribute to the development of reliable diagnostic tools that empower medical professionals in the fight against breast cancer. The paper concludes by summarizing key findings. Additionally, the model achieved an accuracy of 84%, highlighting its effectiveness in breast cancer detection. The paper also emphasizes the broader significance of leveraging deep learning for enhanced breast cancer detection.

I. Introduction

Breast cancer is a serious concern throughout the world. It is the second leading cause of death by cancer for women worldwide [1]. In 2018 alone, there have been 2,1 million new cases of breast cancer, and the rate has increased 3,1% per year [2]. Early-stage breast cancer is considered curable, so correct and early diagnosis of breast cancer is the main key to successful treatment [3]. Medical images, obtained using mammograms and dynamic thermograms, are very important in identifying breast cancer [4]. But, in understanding these images, there are often variations in opinion and human error can occur.