## 1 Introduction
The models were used to extract rich multi-resolution features from four resolutions (40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\)) of histopathological breast cancer images. The rich multiresolution features were then pooled using global average pooling to create an array of deep multiresolution convolutional features, SVM classifier performs multiclassification (8 sub-types) of malignant and benign tumors. SVM algorithms always converge at a global minimum when provided with a suitable feature set irrespective of the dimensionality of the inputs. The target malignant breast cancer classes are ductal carcinoma in situ, lobular carcinoma, mucinous carcinoma, and papillary carcinoma subtypes, and the benign breast cancer target classes are adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma subtypes. Methods for extracting image features fall into three main categories [20]: (1) Automatic feature extraction using deep learning, (2) handcrafted features, and (3) unsupervised feature learning. Manual feature extraction is tedious and error-prone. EffictiNetb0 is the baseline model for EfficientNet, which uses compound scaling, a novel scaling method, to scale the model's dimensions uniformly to increase performance. With ResNet-50, deep residual networks are constructed with 50 layers of residual blocks, which mitigates the vanishing gradient descent problem to maintain accuracy as the depth of the network increases. In contrast to the previously described neural networks, ResNet18 is a gateless or open-gated variant of the highway-net, the first working very deep feedforward neural network. Some layers can be jumped over by using skip connections. In typical implementations, it includes double- or triple-layer skips with nonlinearities and batch normalizations. DenseNet-201 is a convolutional neural network that is 201 layers deep. It utilizes dense connections between layers through dense blocks, where all layers are connected directly. Each layer receives additional inputs from all preceding layers and passes its feature maps to all subsequent layers to preserve the feed-forward nature. The feature extraction step is fundamental to the analysis of medical images using machine learning, and a variety of extraction strategies have been proposed in the past for the classification of various diseases using images [21; 22; 23; 24]. In summary, the proposed method utilizes the power of pre-trained, state-of-the-art DCNN models to extract a multi-scale pooled image feature representation (MPIFR) from four resolutions (40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\)) of BC images. The proposed MPIFR is a highly predictive auto-learned representation that is then classified using SVM. In rigorous evaluation, the proposed MPIFR achieved an average accuracy of 97.77%, with 97.48% sensitivity, and 98.45% precision on the BreaKHis dataset [25]. The proposed ensemble approach outperforms a comprehensive set of state-of-the-art CNN baselines and the prior state-of-the-art for classifying multiresolution (40\(\times\), 100\(\times\), 200\(\times\), and 400\(\times\)) histopathological breast cancer images including ResNet18, InceptionV3, DenseNet201, EfficientNetb0, SqueezeNet, and ShuffleNet. Our evaluation demonstrates that every component of MPIFR contributes non-trivially to its superior performance, including transfer learning (pre-training and fine-tuning), deep feature extraction at multiple resolutions into a powerful feature representation and classification using one-versus-one SVM. Challenges: Firstly, due to the heterogeneous visual texture patterns in breast histopathological images, DCNNs are challenged to reliably classify tumor malignancy, which negatively impacts their performance. Secondly, the most predictive features that discriminate malignant and benign breast cancers in histopathological images may appear at different resolutions, which differ for various BC cases.