###### Abstract
After pairwise multiplication and summation by the DMAS algorithm, the intensity of the focal point is high and, therefore, generates clutter on the image. The CF-DAS and CF-DMAS algorithms use an adaptive weighting technique [18], introducing a coherence factor (CF) [19] that reflects the degree of focusing of the signal and using it to weight the intensity values at each focal point in the image [20; 21]. This weighting ensures that intensity values of focus points with high CF are preserved while suppressing the intensity of focus points with low CF. However, it is important to note that at some focal points where tumors are not present, the scattered signals from tumors in different channels do not overlap in time after reverse time delays. The signal intensity cannot be canceled by summing, resulting in a high CF value. In addition, in some focal points without tumors, the signal of certain different channels are aligned peak-to-peak and valley-to-valley, with high coherence between the signals, and the signal intensity after summing and the CF value are high. After CF weighting, the intensity of a focal point without a tumor is retained, which can cause clutter in the image. To further reduce clutter in the image, in this study, a microwave breast tumor detection system was designed that uses a multiplicative improved coherence factor delay multiply and sum (MICF-DMAS) algorithm based on the maximum interclass difference method (OTSU) algorithm [22; 23]. OTSU is a nonparametric unsupervised adaptive image segmentation method in which an image is segmented into different regions by identifying the appropriate threshold value, through statistical analysis, for minimizing the variance of intensity values in the same region and maximizing the variance in different regions [24]. In this paper, first, the OTSU algorithm is used to distinguish between the scattering signals of tumors and other signals in the obtained signals, and the starting and ending moments of the tumor scattering signals for each channel are then identified to find pairs of tumor scattering signals that did not overlap in time for different channels. The absolute value of the signal intensity is taken as the gray value of the image, and the OTSU algorithm is used to obtain the optimal threshold to divide the signal into two parts with different intensity. The first local minimum to the left of the first point in the signal that exceeds the threshold is used as the starting moment of the tumor signal. The first local minimum to the right of the last point in the signal that exceeds the threshold is taken as the moment of termination of the tumor signal. The temporal overlap between tumor scattering signals from different channels is calculated by comparing the start and stop moments of tumor signals from different channels. Since the tumor-scattered signals in the signal pairs do not overlap in time, the coherence from summing the signal pairs is higher and, hence, the CF value of the signal pairs is also higher. For pairs of signals with non-overlapping tumor scattering signals, we take the start and end time of the tumor signal received by one of the channels as the start and end time of the time window and multiply the summed signal with the corresponding element of the time window. The values of the signals inside the window remain constant, and the values of the signals outside the window become 0. This reduces the coherence sum of the signal pairs and results in a lower improved coherence factor (ICF). Then, the ICF of several signal pairs is multiplied to obtain the MICF. At some focal points where tumors are not present, some of the channels have high signal coherence and high ICF, while others have low signal coherence and low ICF. The total MICF is multiplied by a small ICF for each occurrence of a signal pair with low coherence, which reduces the MICF in focal points without tumors. In this study, we applied the proposed algorithm to a fabricated tumor model, which was imaged using the imaging system described in Section 3.