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Go to Editorial ManagerBreast cancer is one of the greatest frequent tumours among females in Iraq. Medical ultrasound imaging has become a common modality for breast tumour imaging because of its ease of use, low cost, and safety. In the present study, Convolutional Neural Network (CNN) feature extraction approaches were used to classify breast ultrasound imaging. The CNN model used is composed of four-layer for breast cancer ultrasound image analysis. Two types of free datasets were used. These data were divided into groups A and B. Group A has three classes, namely benign, malignant and normal, while group B has two classes, namely, benign and malignant. The proposed technique was assessed based on its accuracy, precision, F1 score and recall. The model's classification accuracy for data A was 96%, whereas for data B was 100%.
The aim of this work is to use Fiber Bragg Grating (FBG) to detect the breast cancer at its earliest stages based on the Photoacoustic (PA) hybrid technique. The fiber Bragg gratings sensitivity to acoustic wave, effect of grating length, effect of grating refractive index modification, and ultrasonic frequency on the wavelength sensitivity and intensity sensitivity of the ultrasonic sensor (FBG) for ultrasonic waves were investigated using a simulation programs. A wavelength for the photoacoustic (PA) excitation laser was chosen with respect to a high absorption by the tumor and with low absorption to the surrounding tissue (normal tissue); for higher contrast absorption between them. Fiber Bragg can be used as a sensor to detect the acoustic wave emitted from the tumor (depending on the photoacoustic principle). In this study, k-wave a MATLAB toolbox was used to simulate photoacoustic wave which is detected with fiber Bragg grating simulation, using Optisystem program. The acoustic wave was transferred to FBG by using Optisystem-MTLAB communication programs to detect tumors.