@article{discovery10185511,
           month = {April},
          volume = {36},
       publisher = {Springer Science and Business Media LLC},
            year = {2024},
           title = {An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images},
         journal = {Neural Computing and Applications},
           pages = {5673--5693},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
          author = {Sharma, Shallu and Kumar, Sumit and Sharma, Manoj and Kalkal, Ashish},
        abstract = {Histopathological diagnosis is the mainstay of present-day preventive medical care service to guide the therapy and treatment of breast cancer at an early stage. Manual examination of histologic data based on clinicians' subjective knowledge is a time-consuming, labour-intensive, and costly method that necessitates clinical intervention and competence for a fair decision. In the recent work, we have developed an ensemble of five deep CNNs to classify three grades of breast cancer using quantitative image-based assessment of digital pathology slides without any manual intervention. To produce final predictions on the dataset, a fuzzy ranking algorithm is used. On the Databiox dataset, the suggested model attained an accuracy of 79\%, 75\%, 89\%, and 82\% at 4{$\times$}, 10{$\times$}, 20{$\times$}, and 40{$\times$} magnification, respectively. Furthermore, it has been observed that the stain-normalization strategy improves the model's classification performance on the histopathological images. In this case, the Macenko stain-normalization technique is employed which further enhances the performance of the proposed ensemble model up to 80\%, 100\%, 100\%, and 82\% at 4{$\times$}, 10{$\times$}, 20{$\times$}, and 40{$\times$} magnification, respectively. Additionally, a comparative analysis with the existing state-of-the-art technique demonstrated the superiority of the proposed scheme.},
             url = {http://dx.doi.org/10.1007/s00521-023-09368-1},
        keywords = {Breast cancer; Ensemble model; Pathology; CNN; Stain-normalization},
            issn = {0941-0643}
}