SavedAdd your journal

Journals / The Journal of Cognitive Systems / 2021 / Cilt: 6 - Sayı: 2

BREAST CANCER DIAGNOSIS BASED ON THERMOGRAPHY IMAGES USING PRE-TRAINED NETWORKS

Hasan UCUZAL
Muhammet BAYKARA
Zeynep KÜÇÜKAKÇALI
Pages
64–68
Publication Date
2021-12-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of the different pre-trained network models after the data preprocessing stages, the best classification performance was achieved for the ResNet50V2 model with an accuracy value of 0.996.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: Breast cancer is the leading cause of death among women around the world.
📊
Key Findings & Data:The data set consists of 179 healthy images and 101 images from patients.
💡
Conclusion & Impact:According to the results of the different pre-trained network models after the data preprocessing stages, the best classification performance was achieved for the ResNet50V2 model with an accuracy value of 0.996.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Classification · breast cancer · deep learning

Abstract

Aim: Breast cancer is the leading cause of death among women around the world. Because of its low cost and the fact that it does not emit hazardous radiation, infrared thermography has emerged as a viable approach for diagnosing the condition in young women. This study aims to create a computer-aided diagnostic system that can process thermographic breast cancer images and classify breast cancer with pre-trained networks in order to use thermography as a diagnostic method. Materials and Methods: In this study, an open-access data set consisting of thermographic breast cancer images was used for diagnostic purposes. The data set consists of 179 healthy images and 101 images from patients. The images were converted from .txt format to .jpeg format. The data set is acquired from http://visual.ic.uff.br/dmi/. In this study, various pre-trained networks were used to reduce the training time. Different metrics were employed to assess the performance of the models. Results: The images obtained during the modeling phase were used to display both breasts in the image without distinguishing the right and left breasts, that is, without fragmenting the images. According to the results of the different pre-trained network models after the data preprocessing stages, the best classification performance was achieved for the ResNet50V2 model with an accuracy value of 0.996. Conclusion: In this study, a computer-aided diagnosis system was created by developing an interface for breast cancer classification from thermographic images in addition to experimental findings. The web software based on the proposed models has provided promising predictions of breast cancer from thermographic images. The developed software can help medical and other healthcare professionals easily spot breast cancer.

🔖

Cite This Article

UCUZAL, H., BAYKARA, M., KÜÇÜKAKÇALI, Z. (2021). BREAST CANCER DIAGNOSIS BASED ON THERMOGRAPHY IMAGES USING PRE-TRAINED NETWORKS. The Journal of Cognitive Systems, Cilt: 6 - Sayı: 2, 64–68. https://ajindex.com/articles/806210

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. S. D. Brookman-May et al., "Challenges, hurdles and possible approaches to improve cancer care in developing countries–A short breakdown of the status quo and future perspective," Advances in Modern Oncology Research, vol. 3, no. 5, pp. 204-212, 2017
  2. S. Chakraborty and T. Rahman, "The difficulties in cancer treatment," ecancermedicalscience, vol. 6, 2012
  3. S. Ekici and H. Jawzal, "Breast cancer diagnosis using thermography and convolutional neural networks," Medical hypotheses, vol. 137, p. 109542, 2020
  4. T. Li et al., "The association of measured breast tissue characteristics with mammographic density and other risk factors for breast cancer," Cancer Epidemiology and Prevention Biomarkers, vol. 14, no. 2, pp. 343-349, 2005
  5. J. Zuluaga-Gomez, N. Zerhouni, Z. Al Masry, C. Devalland, and C. Varnier, "A survey of breast cancer screening techniques: thermography and electrical impedance tomography," Journal of medical engineering & technology, vol. 43, no. 5, pp. 305-322, 2019
  6. S. G. Kandlikar et al., "Infrared imaging technology for breast cancer detection–Current status, protocols and new directions," International Journal of Heat and Mass Transfer, vol. 108, pp. 2303-2320, 2017
  7. Ç. Cabıoğlu and H. Oğul, "Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning," in International Work-Conference on Bioinformatics and Biomedical Engineering, 2020, pp. 716-726: Springer
  8. E. Chaves, C. B. Gonçalves, M. K. Albertini, S. Lee, G. Jeon, and H. C. J. A. O. Fernandes, "Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images," vol. 59, no. 17, pp. E23-E28, 2020
  9. J. C. Torres-Galván, E. Guevara, E. S. Kolosovas-Machuca, A. Oceguera-Villanueva, J. L. Flores, and F. J. J. Q. I. T. J. González, "Deep convolutional neural networks for classifying breast cancer using infrared thermography," pp. 1-12, 2021
  10. G. Chartrand et al., "Deep learning: a primer for radiologists," Radiographics, vol. 37, no. 7, pp. 2113-2131, 2017
  11. V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Icml, 2010
  12. S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1-6: Ieee
  13. Ö. İnce, I. Senel, and F. Yılmaz, "Image Processing and Analysis in Health: Advantages, Challenges, Threats and Examples," Archives of Health