International Conference on Computer and Knowledge Engineering

Home / 13th International Conference on Computer and Knowledge Engineering

An Efficient Approach for Breast Abnormality Detection through High-Level Features of Thermography Images

Authors :

Farhad Abedinzadeh Torghabeh¹ Yeganeh Modaresnia² Seyyed Abed Hosseini³

1- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran 2- Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran 3- Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

Keywords :

breast abnormalities،thermal imaging،deep transfer learning

Abstract :

Breast cancer is a significant global health concern affecting millions of women worldwide. Timely detection is paramount in improving prognosis and survival rates. In this context, infrared thermography has emerged as a promising noninvasive imaging modality for breast cancer diagnosis. This study used raw and pre-processed images using contrast-limited adaptive histogram equalization (CLAHE) and contrast enhancement techniques. One of the key challenges in analyzing breast infrared thermogram images is extracting meaningful features that can aid in accurate diagnosis. To address this, three well-known pre-trained convolutional neural networks, such as AlexNet, GoogLeNet, and SqueezeNet, were used to extract high-level features automatically. Subsequently, the resulting features were subjected to the principal component analysis and the top 100 features were selected, which were then utilized as input for supervised learning classifiers. The proposed method was validated using a publicly available DMR dataset of 100 healthy and 100 abnormal breast thermal images. Notably, the proposed methodology achieves an outstanding accuracy of 99.60% and sensitivity of 100% by employing AlexNet features derived from CLAHE pre-processed images in conjunction with a decision tree classifier. These results underscore the efficacy of the proposed approach in accurate breast cancer detection using infrared thermography.

Papers List

List of archived papers

A Federated Learning-Based Hybrid Deep Learning Framework for Enhanced Human Activity Recognition

Jamileh Azmoudeh - Sajjad Arghaee - Parisa Valizadeh - Samaneh Dandani - Iman Havangi - Mohammad Hossein Yaghmaee

An Overview of Regression Methods in Early Prediction of Movie Ratings

Houmaan Chamani - Zhivar Sourati Hassanzadeh - Behnam Bahrak

Attention Transfer in Self-Regulated Networks for Recognizing Human Actions from Still Images

Masoumeh Chapariniya - Sara Vesali Barazande - Seyed Sajad Ashrafi - Shahriar B.Shokouhi

Identification of Botnets and Nodes Attacking Smart Cities by Majority Voting Mechanism and Feature Selection

Maliheh Araghchi - Nazbanoo Farzaneh

Dynamic Knowledge Enhanced Neural Fashion Trend Forecasting with Quantile Loss

Fatemeh Rooholamini - Reza Azmi - Mobina Khademhossein - Maral Zarvani

A scalable blockchain-based educational network for data storage and assessment

Maryam Fattahi Vanani - Hamidreza Shayegh Borujeni - Ali Nourollah

A New Time Series Approach in Churn Prediction with Discriminatory Intervals

Hedieh Ahmadi - Seyed Mohammad Hossein Hasheminejad

Using Deep Learning for Classification of Lung Cancer on CT Images in Ardabil Province

Mohammad Ali Javadzadeh Barzaki - Jafar Abdollahi - Mohammad Negaresh - Maryam Salimi - Hadi Zolfeghari - Mohsen Mohammadi - Asma Salmani - Rona Jannati - Firouz Amani

Fast and Accurate Motif Discovery in Protein Sequences Using Parallel Processing with OpenMP

Rahele Mohammadi - Mahmoud Naghibzadeh - Abdorreza Savadi

Dual Memory Structure for Memory Augmented Neural Networks for Question-Answering Tasks

Amir Bidokhti - Shahrokh Ghaemmaghami

Samin Hamayesh - Version 41.7.6