###### Abstract  
In the medical field, microwave imaging technology has experienced rapid development due to its non-invasive and non-radioactive nature. The confocal algorithm is a method commonly used for microwave breast cancer imaging, with the key objective of removing clutter in images to achieve high-quality results. However, the current methods are facing challenges in removing clutter. In order to reduce the clutter in images, a multiplicative improved coherence factor delay multiply and sum algorithm based on the maximum interclass differencing method is proposed. The algorithm compares the starting and ending moments of tumor signals in different channels to determine whether the tumor-scattered signals in different channels overlap in time. An improved coherence coefficient is obtained by summing the non-overlapping signals and multiplying the time window. The multiplicative improved coherence factor, which is obtained by multiplying the coherence coefficients of the improved multi-pair signals, is then multiplied by the focal point intensity obtained using the delay multiply and sum algorithm to reduce clutter in an image. To evaluate the performance of the proposed algorithm, several low-cost uniform and non-uniform models of human breast and tumor tissue with dielectric properties were prepared for testing. The experimental results show that, compared to the existing algorithm, the proposed algorithm can greatly reduce the clutter in images, with a signal-to-clutter ratio of at least 4 dB higher as well as contrast at least six-fold higher. breast cancer detection; confocal imaging; electromagnetic microwave imaging (EMI); CF-DAS algorithm +
Footnote †: journal: Medicalapproaches (microwave tomography) [7]. In microwave tomography, a profile of breast electrical properties is recovered from measurements using near-field tomographic image reconstruction algorithms. However, a requirement for microwave tomography is to solve complex inverse scattering problems, which is dependent on numerical analysis methods that are computationally intensive and slow when applied in imaging [8]. Confocal microwave imaging (CMI) is an algorithm commonly used for detecting breast cancer using UWB microwave imaging [9; 10]. When a UWB microwave signal wave is directed toward breast tissues, the tissue properties, such as the dielectric constant and conductivity, cause different tissues to reflect the scattered wave signal at their boundaries. These scattered signals are acquired and used by the CMI algorithm for reconstructing images [6]. CMI does not depend on reconstruction of the media distribution within the entire imaging area and only on localizing anomalies such as tumors. In this way, the complicated inverse scattering equation is avoided, and, thus, calculation is simple and imaging rapid [11]. The delay and sum (DAS) algorithm is simple to implement and is one of the most basic and commonly used imaging algorithms in CMI [12; 13]. However, the DAS algorithm yields images of poorer quality with more clutter [14]. Clutter around the tumor may mask the presence of multiple other tumors near the investigated tumor, and high-intensity clutter may be misidentified as a tumor [7]. Therefore, reducing the clutter in images is important in CMI. The delay multiply and sum (DMAS) algorithm [7], the coherence factor delay and sum (CF-DAS) algorithm [15], and the coherence factor delay multiply and sum (CF-DMAS) algorithm [16] have been proposed to enhance image quality. The DMAS algorithm introduces a paired multiplication method based on the DAS algorithm, where the signals from different channels at each focal point are multiplied in pairs after a reverse time delay and then summed to obtain the intensity of each focal point [17]. However, after the time delay, at certain focal points where there is no tumor, the peaks of the signals from different channels are aligned with the peaks and the troughs with the troughs.