## 1 Introduction
The proposed MPIFR approach innovatively addresses these two challenges, making it particularly appropriate for discriminating between BC tumor malignancies. Related work that utilized deep learning and CNNs for breast cancer tumor multi-classification are summarized in Table 1. The deep multiresolution feature representation for 8-classes, which we propose, has not been explored previously for breast cancer classification using neural networks and SVM. [PERSON] et al. in [26] performed multi-class breast cancer classification from histopathology images using 6B-Net deep CNN model, with feature fusion and selection mechanism. The method achieved a multi-class average accuracy of 94.20% for 4-class and 90.00% for 8-class, respectively, on histopathological images. [PERSON] et al. proposed a breast cancer multiclassification from histopathological images with a structured deep learning model [27], the model achieved an accuracy of 93.2%. [PERSON] et al. [28] utilized GoogleNet architecture [29] to classify histopathology images into subtypes using majority voting. MUDeRN investigated using ResNet [17] to classify breast cancer images into malignant or benign and further categorized each to its subsequent subtypes using two modules M and B [30]. [PERSON] et al. in [31] used handcrafted features extracted to train the DNN classifiers with four dense layers and the SoftMax layer. [PERSON] et al. in [32] performed 4-class classification based on magnification factor, using some deep learning models. Related work that used CNNs to extract deep features from medical images [PERSON] et al. proposed an automated mammographic image detection system using a CNN for feature extraction and SVM for classification but did not explore multi-resolution extraction and pooling [35]. [PERSON] et al. [36] used CNN models to detect pneumoconiosis in X-ray images by extracting deep multi-level features. [PERSON] et al. [37] present a systematic review of computer-aided diagnosis of coal workers' pneumoconiosis in chest X-rays using machine learning, which included approaches that used CNNs. [PERSON] et al. [38] used CheXNet-model as part of an ensemble of multi-dimensional deep feature extractors from chest X-rays to detect and visualize pneumoconiosis. To convert the output of the model into one-dimensional vectors, the last layer close to the output layer was removed, then a global average pooling layer was added. [PERSON] 162 et al. [39] used computer-aided diagnosis (CADx) systems, to examine the optimal point for extracting features from pre-trained CNNs. [PERSON] et al. [40] proposed ensemble learners for pulmonary nodule classification by combining deep CNNs. Other related research includes work by [PERSON] et al. [41] who previously used adaptive boosting (AdaBoost), an ensemble method, to combine multiple weak classifiers into a single classifier. Using k-means with K = 4000, each tissue image generated 4000-Teton histograms as features. An accuracy of 80% was achieved for multi-class classification (three target classes: I, II, and benign). [PERSON] and [PERSON] [33] proposed a binary classifier using a transfer learning model ResNet-50 CNN and achieved a performance accuracy of 99% using histopathological images. [PERSON] et al. [42] and [PERSON] et al. [20] previously extracted nuclei feature from fine needle biopsies. First, the circular Hough transform was utilized for detecting nuclei candidates and false-positive reduction, followed by machine learning and Otsu thresholding.