Microscopic imaging aids disease diagnosis by describing quantitative cell morphology and tissue size. However, the high spatial resolution of these images poses significant challenges for manual quantitative evaluation. This project proposes using computer-aided analysis methods to address these challenges, enabling rapid and precise clinical diagnosis, course analysis, and prognostic prediction. This research introduces advanced deep learning frameworks such as squeeze-and-excitation and dilated dense convolution blocks to tackle the complexities of quantifying small and intricate breast cancer tissues and meeting the real-time requirements of pathological image analysis. Our proposed framework integrates a dense convolutional network (DenseNet) with an attention mechanism, enhancing the capability for rapid and accurate clinical assessments. These multi-classification models facilitate the precise prediction and segmentation of breast lesions in microscopic images by leveraging lightweight multi-scale feature extraction, dynamic region attention, sub-region classification, and regional regularization loss functions. This research will employ transfer learning paradigms and data enhancement methods to enhance the models' learning further and prevent overfitting. We propose the fine-tuning employing pre-trained architectures such as VGGNet-19, ResNet152V2, EfficientNetV2-B1, and DenseNet-121, modifying the final pooling layer in each model's last block with an SPP layer and associated BN layer. The study uses labeled and unlabeled data for tissue microscopic image analysis, enhancing models' robust features and classification abilities. This method reduces the costs and time associated with traditional methods, alleviating the burden of data labeling in computational pathology. The goal is to provide a sophisticated, efficient quantitative pathological image analysis solution, improving clinical outcomes and advancing the computational field. The model, trained, validated, and tested on a microscope breast image dataset, achieved recognition accuracy of 99.6% for benign and malignant secondary classification and 99.4% for eight breast subtypes classification. Our proposed approach demonstrates substantial improvement compared to existing methods, which generally report lower accuracies for breast subtype classification ranging between 85% and 94%. This high level of accuracy underscores the potential of our approach to provide reliable diagnostic support, enhancing precision in clinical decision-making.