Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms. Issue 1 (1st February 2019)

• Record Type: Journal Article
• Authors: Zade, Maryam Arab; Khodadadi, Hamed
• Is Part Of: IET systems biology. Volume 13:Issue 1(2019)
• Page Start: 1
• Page End: 7
• Publication Date: 2019-02-01
• Subjects: closed loop systems -- fractals -- infrared imaging -- tumours -- fuzzy control -- medical image processing -- cancer -- biological tissues -- gynaecology -- patient treatment; cancer tumours -- breast thermograms -- high‐precision infrared cameras -- image processing -- cancerous tissue -- cancerous lesion -- nonlinear indices -- type II fuzzy controller -- closed‐loop control -- fuzzy controller design -- breast cancer treatment -- 2D fractal dimensions; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2018.5020
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : In this study, three non‐linear indices consist of compression, one‐dimensional (1D) and two‐dimensional (2D) fractal dimensions are used for the determination of the malignancy or benignity of cancer tumours in breast thermograms. On the other hand, by developing the high‐precision infrared cameras as well as new methods of image processing, biomedical thermography images have found a prominent position among the others. Furthermore, cancerous tissue can be affected by the laser. In this study, in order to treat the cancerous lesion identified by breast thermograms, the laser parameters are designed. The basis of controller designing is the obtained non‐linear indices. If the indices are moved from the chaotic behaviour to normal condition, the treating tissue is going from cancerous to a healthy condition and the treatment process is completed. Radiation frequency and the energy density of laser are designed as two key elements in the cancer treatment. In this study, the type I and type II fuzzy controllers are employed for the control strategies. Using the proposed closed‐loop control, the non‐linear indices of the cancerous lesion will be reduced during the treatment process. The simulation results on two datasets of breast thermograms indicate the superiority of type II fuzzy controller.