A systematic review of breast cancer detection using thermography and neural networks

• Main Authors: Al Husaini, Mohammed Abdulla Salim, Habaebi, Mohamed Hadi, Hameed, Shihab A., Islam, Md. Rafiqul, Gunawan, Teddy Surya
• Format: Article
• Language: English; English; English
• Published: IEEE 2020
• Subjects: TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
• Online Access: http://irep.iium.edu.my/85052/1/09261422%20Online%20early%20access%20vers.pdf; http://irep.iium.edu.my/85052/7/85052_A%20systematic%20review%20of%20breast%20cancer_Scopus.pdf; http://irep.iium.edu.my/85052/8/85052_A%20systematic%20review%20of%20breast%20cancer.pdf; http://irep.iium.edu.my/85052/; https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9261422

Breast cancer plays a significant role in affecting female mortality. Researchers are actively seeking to develop early detection methods of breast cancer. Several technologies contributed to the reduction in mortality rate from this disease, but early detection contributes most to preventing disease spread, breast amputation and death. Thermography is a promising technology for early diagnosis where thermal cameras employed are of high resolution and sensitivity. The combination of Artificial Intelligence (AI) with thermal images is an effective tool to detect early stage breast cancer and is foreseen to provide impressive predictability levels. This paper reviews systematically the related works employing thermography with AI highlighting their contributions and drawbacks and proposing open issues for research. Several different types of Artificial Neural Networks (ANNs) and deep learning models were used in the literature to process thermographic images of breast cancer, such as Radial Basis Function Network (RBFN), K-Nearest Neighbors (KNN), Probability Neural Network (PNN), Support Vector Machine (SVM), ResNet50, SeResNet50, V Net, Bayes Net, Convolutional Neural Networks (CNN), Convolutional and DeConvolutional Neural Networks (C-DCNN), VGG-16, Hybrid (ResNet-50 and V-Net), ResNet101, DenseNet and InceptionV3. Previous studies were found limited to varying the numbers of thermal images used mostly from DMR-IR database. In addition, analysis of the literature indicate that several factors do affect the performance of the Neural Network used, such as Database, optimization method, Network model and extracted features. However, due to small sample size used, most of the studies achieved a classification accuracy of 80% to 100%.