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Current systems rely on imaging technology, but digital pathology and computerized analysis can enhance accuracy, reduce false predictions, and improve medical care for breast cancer patients. The study explores the challenges in identifying benign and malignant breast cancer lesions using microscopic image datasets. It introduces a low\u2010dimensional multiple\u2010channel feature\u2010based method for breast cancer microscopic image recognition, overcoming limitations in feature utilization and computational complexity. The method uses RGB channels for image processing and extracts features using level co\u2010occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. This approach aims to improve diagnostic efficiency and accuracy in breast cancer treatment. The core of our method is the SqE\u2010DDConvNet algorithm, which utilizes a 3\u2009\u00d7\u20091 convolution kernel, SqE\u2010DenseNet module, bilinear interpolation, and global average pooling to enhance recognition accuracy and training efficiency. Additionally, we incorporate transfer learning with pre\u2010trained models, including mVVGNet16, EfficientNetV2B3, ResNet101V2, and CN2XNet, preserving spatial information and achieving higher accuracy under varying magnification conditions. The method achieves higher accuracy compared to baseline models, including texture and deep semantic features. This deep learning\u2010based methodology contributes to more accurate image classification and unique image recognition in breast cancer microscopic images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Research Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Introduces a low\u2010dimensional multiple\u2010channel feature\u2010based method for breast cancer microscopic image recognition.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Uses RGB channels for image processing and extracts features using level co\u2010occurrence matrix, wavelet, Gabor, and histogram of oriented gradient.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Employs the SqE\u2010DDConvNet algorithm for enhanced recognition accuracy and training efficiency.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Transfer learning with pre\u2010trained models preserves spatial information and achieves higher accuracy under varying magnification conditions.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Evaluates predictive efficacy of transfer learning paradigms within microscopic analysis.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Utilizes CNN\u2010based pre\u2010trained algorithms to enhance network performance.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/jemt.24557","type":"journal-article","created":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T03:49:06Z","timestamp":1711770546000},"page":"1862-1888","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer learning technique"],"prefix":"10.1002","volume":"87","author":[{"given":"Amjad","family":"Rehman","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University  Riyadh 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