A multi-scale adaptive filtering and AtRes_SRU–transformer synergy for breast cancer histopathology classification

The Biomedical image analysis is also crucial in the contemporary healthcare system as it facilitates correct diagnosis of diseases, treatment planning, and clinical decision-making. As novel imaging methods like MRI, histopathology and chest X-ray have appeared, the necessity of automated systems that could effectively work with extensive image data of different complexity has grown. The main difficulty, however, is to strike the right balance between the complexity of biomedical images and computational efficiency because, most of the time, these images are large-scale, high-resolution, and have a large tissue variation, noise, and inter and intra-class differences. Conventional deep learning models such as CNNs and RNNs cannot handle such issues and at the same time achieve high diagnostic accuracy and computational efficiency particularly in real-time clinical scenarios. The current paper introduces the pseudo-name ImTranNet-TriCore, which is a new model of deep learning developed to facilitate the categorization of biomedical images. The suggested model combines three important innovations including a Learnable Multi-Scale Adaptive Filtering module (LM-AdaFilter), a Dual-Path Attentive Residual SRU (DP-AtRes-SRU) and a Multi-Head Hybrid Transformer (MHHT). The components all contribute to the challenging issues of noise-reduction, spatial-temporal features learning, and global-contextual reasoning in the biomedical images. LM-AdaFilter is a dynamically-adjusted filtering parameter to retain diagnostically important features, whereas DP-AtRes-SRU can capture the spatial as well as the temporal relationships. The MHHT reconciles the local features and global context to increase feature fusion and boost classification accuracy. The main goal of the paper is to suggest the computationally effective and interpretable biomedical image classification model. The ImTranNet-TriCore model was put to test on conventional biomedical datasets and it performed at 95.92 accuracy. Precision (97.83% in Brain MRI, 97.67% in Chest X-ray), Recall (93.75% in Brain MRI, 87.50% in Chest X-ray) and F1-Score indicated the strong performance of the model in distinguishing between positive and negative cases, as well as reducing the number of false positives. The findings emphasize the fact that ImTranNet-TriCore is superior to the conventional models such as CNNs, RNNs, and standalone transformers, in working on complex and noisy biomedical data, and is therefore applicable in the real-life clinical context.