This study presents HistoNet, a refined convolutional neural network based on the EfficientNet B0 architecture optimized explicitly for histopathological image classification. Trained on a diverse array of cancer images, HistoNet showcases exceptional feature extraction capabilities, surpassing established models such as the VGG series, InceptionV3, and the EfficientNet variants, particularly on datasets not encountered during training. Our analysis demonstrates that HistoNet outperforms these conventional models in extracting nuanced features critical for accurate cancer detection. In a series of comparative tests, HistoNet consistently achieved higher validation accuracies, excelling with up to 99.47% in specific cancer subtype classifications, thereby illustrating its superiority over other recently proposed models. The efficacy of HistoNet’s feature selection is further evident in its capacity to enhance classification performance across various cancer types, including those with subtle histological differences. We also present Topo-HistoNet, an advanced feature extraction methodology, integrating the HistoNet deep learning-based approach with traditional image analysis techniques, and establishing a new standard for automated histopathological image analysis. The results of this research not only bolster the role of tailored deep learning models in biomedical imaging but also pave the way for their application in broad diagnostic contexts, potentially extending to unexplored tumor categories.