Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Kuljeet; Sudershan, Amrit; Kumar, Sachin; Shastri, Sourabh; Mansotra, Vibhakar Title A name given to the resource BC-MBINet: A Novel Architecture for Accurate Classification of Breast Cancer with Microscopic Biopsy Images Using Deep Convolutional Neural Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Pattern Recognition and Artificial Intelligence;Volume;39;Issue;13;Article No.;2557015; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0218001425570150" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0218001425570150</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012558292?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012558292?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh K., Department of Computer Science, School of Sciences, Christ University, NCR, Delhi, 201003, India; Sudershan A., Department of Human Genetics, Institute of Human Genetics, University of Jammu, Jammu &amp; Kashmir, 180006, India; Kumar S., Department of Computer Science &amp; IT, University of Jammu, Jammu &amp; Kashmir, 180006, India; Shastri S., Department of Computer Science &amp; IT, University of Jammu, Jammu &amp; Kashmir, 180006, India; Mansotra V., Department of Computer Science &amp; IT, University of Jammu, Jammu &amp; Kashmir, 180006, India Description An account of the resource Breast cancer (BC) is the second most frequent malignancy, accounting for roughly 25% of all cases of cancer. BC is caused by genetic, epigenetic, and environmental factors, and their interaction too. The diagnosis of a BC is a critical step in the treatment process, and histopathological imaging is required to determine the type of illness. Identifying a disease is an important stage in the treatment process. However, this time-consuming task is exhausting, and people are prone to making mistakes that go unnoticed, making it difficult to determine the severity of the condition and this diagnosing step also relies on a pathologists expertise. In this paper, we have developed a novel BC with microscopic biopsy images network (BC-MBINet) model using deep convolutional neural networks. Feature extraction is handled by a sequence of convolutional layers, nonlinearity is handled using LeakyReLU activations, and learning is stabilized by batch normalization. A last Softmax layer is employed for binary classification into benign and malignant tumors, and dropout layers are included to decrease overfitting. The model achieves state-of-the-art accuracy and resilience in discriminating BC types by being trained on a publically available dataset of microscopic biopsy images. The proposed model is capable of classifying between the benign and malignant BC tumors with 99.04% accuracy. The model gives state-of-the-art results in its accuracy in classifying BC tumors into Benign or Malignant. 2025 World Scientific Publishing Company. Subject The topic of the resource Breast Cancer; classification; deep learning; histopathological images; microscopic biopsy Publisher An entity responsible for making the resource available World Scientific Relation A related resource ISSN: 2180014; CODEN: IJPIE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online