A systematic review of breast cancer detection on thermal images

Main Article Content

Aqil Aqthobirrobbany
Dian Nova Kusuma Hardani
Indah Soesanti
Hanung Adi Nugroho


Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.


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How to Cite
Aqil Aqthobirrobbany, Dian Nova Kusuma Hardani, Indah Soesanti, & Adi Nugroho, H. (2023). A systematic review of breast cancer detection on thermal images. Communications in Science and Technology, 8(2), 216-225. https://doi.org/10.21924/cst.8.2.2023.1270
Author Biography

Hanung Adi Nugroho, Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadja Mada, Yogyakarta 55281, Indonesia

Assistant Professor

Department of Electrical Engineering and Information Technology


1. S. Civilibal, K. K. Cevik, A. Bozkurt, A deep learning ap-proach for automatic detection, segmentation and classi-fication of breast lesions from thermal images, Exp. Sys. with Appli. 212 (2023) 118774.
2. R. Sanchez´-Cauce, J. Perez´-Mart´?n, M. Luque, Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data, Comp. Meth. and Progr. in Biomedic. 204 (2021) 106045.
3. J. Zuluaga-Gomez, Z. A. Masry, K. Benaggoune, S. Mer-aghni, N. Zerhouni, A cnn-based methodology for breast cancer diagnosis using thermal images, Comp. Meth. In Biomech. and Biomed. Eng.: Imaging Visualization 9 (2) (2020) 131–145.
4. C¸. Cab?oglu,? H. Ogul,? Computer-Aided Breast Cancer Di-agnosis from Thermal Images Using Transfer Learning, in: Bioinf. and Biomedic. Eng., Springer International Pub-lishing, 2020, pp. 716–726.
5. M. de Freitas Oliveira Baffa, L. G. Lattari, Convolutional Neural Networks for Static and Dynamic Breast Infrared Imaging Classification, in: 2018 31st SIBGRAPI Conf. on Graph., Patt. and Imag. (SIBGRAPI), IEEE, 2018.
6. H. A. Nugroho, T. Kirana, V. Pranowo, A. H. T. Hutami, Optic cup segmentation using adaptive threshold and morphological image processing, Commun. in Scien. and Techn. 4 (2) (2019) 63–67.
7. L. K, S. B. Dabhade, Y. S. Rode, K. Dabhade, S. Desh-mukh, R. Maheshwari, Identification of Breast Cancer from Thermal Imaging Using SVM and Random Forest Method, in: Proc. of the 5th Int. Conf. on Trends in Elec-tronics and Informatics (ICOEI), IEEE, 2021.
8. A. Khan, A. S. Arora, Classification in Thermograms for Breast Cancer Detection Using Texture Features with Feature Selection Method and Ensemble Classifier, in: Proc. of the Int. Conf. on Issues and Challenges in Intelli-gent Computing Techniques (ICICT), IEEE, 2019.
9. M. A. S. A. Husaini, M. H. Habaebi, S. A. Hameed, M. R. Islam, T. S. Gunawan, A Systematic Review of Breast Can-cer Detection Using Thermography and Neural Networks, IEEE Access 8 (2020) 208922–208937.
10. H. Dihmani, O. Bouattane, O. S. Grief, A Review on Suspicious-Regions Segmentation Methods in Breast Ther-mogram Image, in: Proc. 2nd Int. Conf. on Innovative Res. in Applied Science, Eng., and Tech. (IRASET), IEEE, 2022.
11. R. Roslidar, A. Rahman, R. Muharar, M. R. Syahputra, Arnia, M. Syukri, B. Pradhan, K. Munadi, A Review on Recent Progress in Thermal Imaging and Deep Learning Approaches for Breast Cancer Detection, IEEE Access 8 (2020) 116176–116194.
12. S. Sumi, H. A. Nugroho, R. Hartanto, A Systematic Re-view on Automatic Detection of Plasmodium Parasite, Int. Eng. Technol. Innov. 11 (2) (2021) 103–121.
13. J. Pachouly, S. Ahirrao, K. Kotecha, G. Selvachandran, Abraham, A Systematic Literature Review on Software Defect Prediction Using Artificial Intelligence: Datasets, Data Validation Methods, Approaches, and Tools, Eng. Appl. Artif. Intell. 111 (2022) 104773.
14. P. H. Prastyo, A. S. Sumi, S. S. Kusumawardani, A Sys-tematic Literature Review of Application Development to Realize Paperless Application in Indonesia: Sectors, Plat-forms, Impacts, and Challenges, Indones. J. Inf. Syst. 2 (2) (2020) 111–129.
15. N. Lanisa, N. S. Cheok, L. K. Wee, Color Morphology and Segmentation of the Breast Thermography Image, in: Proc. IEEE Conf. on Biomedical Engineering and Sci-ences (IECBES), IEEE, 2014.
16. P. S, S. C. M, R. S, Asymmetry Analysis of Breast Ther-mograms Using BM3D Technique and Statistical Texture Features, in: Proc. Int. Conf. on Informatics, Electronics Vision (ICIEV), IEEE, 2014.
17. R. Rastghalam, H. Pourghassem, Breast Cancer Detection Using Spectral Probable Feature on Thermography Im-ages, in: Proc. 8th Iranian Conf. on Machine Vision and Image Processing (MVIP), IEEE, 2013.
18. C. B. Gonc¸alves, J. R. Souza, H. Fernandes, CNN architec-ture optimization using bio-inspired algorithms for breast cancer detection in infrared images, Comput. Biol. Med. 142 (2022) 105205.
19. C. B. Goncalves, J. R. Souza, H. Fernandes, CNN opti-mization using surrogate evolutionary algorithm for breast cancer detection using infrared images, in: 2022 IEEE 35th Int. Symp. on Computer-Based Medical Systems (CBMS), IEEE, 2022.
20. S. Shahari, A. Wakankar, Color Analysis of Thermograms for Breast Cancer Detection, in: Proc. Int. Conf. on Indus-trial Instrumentation and Control (ICIC), IEEE, 2015.
21. S. Pramanik, D. Banik, D. Bhattacharjee, M. Nasipuri, K. Bhowmik, G. Majumdar, Suspicious-Region Seg-mentation From Breast Thermogram Using DLPE-Based Level Set Method, IEEE Trans. Med. Imaging 38 (2) (2019) 572–584.
22. Lou, S. Guan, N. Kamona, M. Loew, Segmentation of infrared breast images using multiresunet neural net-works, in: 2019 IEEE Applied Imagery Pattern Recogni-tion Workshop (AIPR), IEEE, 2019.
23. M. Abdel-Nasser, A. Moreno, D. Puig, Breast cancer de-tection in thermal infrared images using representation learning and texture analysis methods, Electron. 8 (1) (2019) 100.
24. S. Pramanik, S. Ghosh, D. Bhattacharjee, M. Nasipuir, Segmentation of breast-region in breast thermogram us-ing arc-approximation and triangular-space search, IEEE Trans. Instrum. Meas. 69 (7) (2020) 4785–4795.
25. E. A. Mohamed, E. A. Rashed, T. Gaber, O. Karam, Deep learning model for fully automated breast cancer detec-tion system from thermograms, PLOS ONE 17 (1) (2022) e0262349.
26. R. M. Prakash, K. Bhuvaneshwari, M. Divya, K. J. Sri, S. Begum, Segmentation of Thermal Infrared Breast Images Using K-means, FCM and EM Algorithms for Breast Cancer Detection, in: Proc. of the Int. Conf. on In-novations in Information, Embedded and Communication Systems (ICIIECS), IEEE, 2017.
27. J. Shu, R. Ding, A. Jin, H. Zhu, S. Chen, Acupoint Se-lection for Autonomous Massage Based on Infrared Ther-mography, Traitement du Signal 39 (1) (2022) 355–362.
28. U. R. Gogoi, G. Majumdar, M. K. Bhowmik, A. K. Ghosh, Bhattacharjee, Breast Abnormality Detection Through Statistical Feature Analysis Using Infrared Thermograms, in: Proc. of the Int. Symp. on Advanced Computing and Communication (ISACC), IEEE, 2015.
29. S. Chatterjee, S. Biswas, A. Majee, S. Sen, D. Oliva, Sarkar, Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method, Comp. in Bio. and Medic. 141 (2022) 105027.
30. V. Madhavi, T. C. Bobby, Thermal Imaging Based Breast Cancer Analysis Using BEMD and Uniform RLBP, in: Proc. of the 3rd Int. Conf. on Biosignals, Images and In-strumentation (ICBSII), IEEE, 2017.
31. M. B. A. Rasyid, Yunidar, F. Arnia, K. Munadi, Histogram Statistics and GLCM Features of Breast Thermograms for Early Cancer Detection, in: Proc. of the Int. ECTI North-ern Section Conf. on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), IEEE, 2018.
32. O. O. Soliman, N. H. Sweilam, D. M. Shawky, Automatic Breast Cancer Detection Using Digital Thermal Images, in: 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), IEEE, 2018.
33. Khan, A. S. Arora, Breast Cancer Detection Through Gabor Filter Based Texture Features Using Thermograms Images, in: 2018 First International Conf. on Secure Cyber Computing and Communication (ICSCCC), IEEE, 2018.
34. S. Mambou, P. Maresova, O. Krejcar, A. Selamat, K. Kuca, Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model, Sensors 18 (9) (2018) 2799.
35. S. Kiymet, M. Y. Aslankaya, M. Taskiran, B. Bolat, Breast Cancer Detection From Thermography Based on Deep Neural Networks, in: 2019 Innovations in Intelligent Sys-tems and Applications Conf. (ASYU), IEEE, 2019.
36. R. Roslidar, K. Saddami, F. Arnia, M. Syukri, K. Mu-nadi, A study of fine-tuning cnn models based on thermal imaging for breast cancer classification, in: 2019 IEEE Int. Conf. on Cybernetics and Computational Intelligence (Cy-berneticsCom), IEEE, 2019.
37. M. A. Farooq, P. Corcoran, Infrared imaging for human thermography and breast tumor classification using ther-mal images, in: 2020 31st Irish Signals and Systems Conf. (ISSC), IEEE, 2020.
38. S. Pramanik, D. Bhattacharjee, M. Nasipuri, O. Krejcar, LINPE-BL: A Local Descriptor and Broad Learning for Identification of Abnormal Breast Thermograms, IEEE Trans. Med. Imaging 40 (12) (2021) 3919–3931.
39. C. B. Goncalves, J. R. Souza, H. Fernandes, Classifica-tion of Static Infrared Images Using Pre-Trained CNN for Breast Cancer Detection, in: Proc. of the 34th IEEE CBMS, IEEE, 2021.
40. M. A. S. A. Husaini, M. H. Habaebi, T. S. Gunawan, M. R. Islam, S. A. Hameed, Automatic Breast Cancer Detection Using Inception V3 in Thermography, in: 2021 8th Int. Conf. on Computer and Communication Engineering (IC-CCE), IEEE, 2021.
41. M.-A. Grigore, V.-E. Neagoe, A Deep CNN Approach Us-ing Thermal Imagery for Breast Cancer Diagnosis, in: 2021 13th Int. Conf. on Electronics, Computers and Ar-tificial Intelligence (ECAI), IEEE, 2021.
42. B. Yousefi, H. Akbari, M. Hershman, S. Kawakita, C. Fernandes, C. Ibarra-Castanedo, S. Ahadian, P. V. Maldague, SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography, Appl. Sci. 11 (7) (2021) 3248.
43. K. Rautela, D. Kumar, V. Kumar, An Interpretable Net-work to Thermal Images for Breast Cancer Detection, in: 2022 Int. Conf. on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE, 2022.
44. R. Resmini, L. F. da Silva, P. R. Medeiros, A. S. Araujo, C. Muchaluat-Saade, A. Conci, A hybrid methodology for breast screening and cancer diagnosis using thermog-raphy, Comput. Biol. Med. 135 (2021) 104553.
45. D. Tiwari, M. Dixit, K. Gupta, Deep Multi-View Breast Cancer Detection: A Multi-View Concatenated Infrared Thermal Images Based Breast Cancer Detection System Using Deep Transfer Learning, Traitement du Signal 38 (6) (2021) 1699–1711.
46. S. Dey, R. Roychoudhury, S. Malakar, R. Sarkar, Screen-ing of breast cancer from thermogram images by edge detection aided deep transfer learning model, Multimed. Tools Appl. 81 (7) (2022) 9331–9349.
47. D. TIWARI, M. DIXIT, K. GUPTA, Breast cancer-caps: a breast cancer screening system based on capsule net-work utilizing the multiview breast thermal infrared im-ages, Turkish J. Electr. Eng. Comput. Sci. 30 (5) (2022) 1804–1820.
48. M. Gezimati, G. Singh, Transfer Learning for Breast Cancer Classification in Terahertz and Infrared Imaging, in: 2022 Int. Conf. on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), IEEE, 2022.
49. M. Ensafi, M. R. Keyvanpour, S. V. Shojaedini, A New method for promote the performance of deep learning paradigm in diagnosing breast cancer: improving role of fusing multiple views of thermography images, Health Technol. 12 (6) (2022) 1097–1107.
50. Alshehri, D. AlSaeed, Breast Cancer Detection in Ther-mography Using Convolutional Neural Networks (CNNs) with Deep Attention Mechanisms, Appl. Sci. 12 (24) (2022) 12922.
51. P. Gomathi, C. Muniraj, P. S. Periasamy, Micro Calcifica-tion Detection in Mammogram Images Using Contiguous Convolutional Neural Network Algorithm, Comput. Syst. Sci. Eng. 45 (2) (2023) 1887–1899.
52. L. F. Silva, D. C. M. Saade, G. O. Sequeiros, A. C. Silva, C. Paiva, R. S. Bravo, A. Conci, A New Database for Breast Research with Infrared Image, J. Med. Imaging Health Inform. 4 (1) (2014) 92–100.
53. C. Ulrich, F. Isensee, T. Wald, M. Zenk, M. Baumgart-ner, K. H. Maier-Hein, MultiTalent: A Multi-Dataset Ap-proach to Medical Image Segmentation (2023). arXiv: 2303.14444.
54. Mishra, K. Aravinda, J. A. Kumar, C. Keerthi, R. D. Shree, S. Srikumar, Medical imaging using signal pro-cessing: a comprehensive review, in: 2022 Second Int. Conf. on Artificial Intelligence and Smart Energy (ICAIS), IEEE, 2022, pp. 623–630.
55. O. Benkarim, C. Paquola, B.-y. Park, V. Kebets, S.-J. Hong, R. Vos de Wael, S. Zhang, B. T. Yeo, M. Eick-enberg, T. Ge, et al., Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging, PLoS Biol. 20 (4) (2022) e3001627.
56. N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, Ding, Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation, Med. Image Anal. 63 (2020) 101693.
57. R. Pomponio, G. Erus, M. Habes, J. Doshi, D. Srini-vasan, E. Mamourian, V. Bashyam, I. M. Nasrallah, T. D. Satterthwaite, Y. Fan, et al., Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan, NeuroImage 208 (2020) 116450.