In this paper, we propose a new multimodal deep learning framework for subclassification of benign breast tumors, which fuses clinical, histopathological, and mammogram image information. The method incorporates a lot of digital signal processing (DSP) solutions in the field of image preprocessing and feature extraction. First-level processing contains noise removal (median filter, Gaussian filter), contrast enhancement (CLAHE, histogram equalization), standardization of intensity, and rescaling. Advanced DSP techniques such as FFT, DWT, and GLCM/LBP are used to extract frequency- domain, time-frequency texture, and local or global characteristics of mammograms. Imputation and one-hot encoding of clinical data. All the characteristics are finally concatenated, and dimensionality is reduced using Principal Component Analysis (PCA), leaving 16 components that express 95% of the variance. Furthermore, a DSP-domain augmentation (frequency noise, wavelet perturbation) increases the amount of data in the dataset. We trained an MLP model on these reduced features, resulting in a test accuracy of 1.0000 and a loss of 0.0116. It is proven that this framework performs effectively in benign breast tumor subclassification, indicating a significant strength of integrated multi-modal data and advanced DSP for the medical image analysis, though more validations on larger databases are encouraged.