Invasive Ductal Carcinoma Breast Cancer (IDC-BC) is the most common type of cancer and its asymptomatic nature has led to an increased mortality rate globally. Advancements in artificial intelligence and machine learning have revolutionized the medical field with the development of AI-enabled computer-aided diagnosis (CAD) systems, which help in determining diseases at an early stage. CAD systems assist pathologists in their decision-making process to produce more reliable outcomes in order to treat patients well. In this work, the potential of pre-trained convolutional neural networks (CNNs) (i.e., EfficientNetV2L, ResNet152V2, DenseNet201), singly or as an ensemble, was thoroughly explored. The performances of these models were evaluated for IDC-BC grade classification using the DataBiox dataset. Data augmentation was used to avoid the issues of data scarcity and data imbalances. The performance of the best model was compared to three different balanced datasets of Databiox (i.e., 1200, 1400, and 1600 images) to determine the implications of this data augmentation. Furthermore, the effects of the number of epochs were analysed to ensure the coherency of the most optimal model. The experimental results analysis revealed that the proposed ensemble model outperformed the existing state-of-the-art techniques in relation to classifying the IDC-BC grades of the Databiox dataset. The proposed ensemble model of the CNNs achieved a 94% classification accuracy and attained a significant area under the ROC curves for grades 1, 2, and 3, i.e., 96%, 94%, and 96%, respectively.

Tissue analysis using histopathological images is the most prevailing as well as a challenging task in the treatment of cancer. The clinical assessment of tissues becomes very tough as high variability in the magnification levels makes the situation worst for any pathologist to deal with the benign and malignant stages of cancer. One of the possible ways to address such a pathetic situation could be an advanced machine learning approach. Hence, a convolutional neural network (CNN) architecture is proposed to create an automated system for magnification independent multi‐classification of breast cancer histopathological images. This automated system offers high productivity and consistency in diagnosing the eight different classes of breast cancer from a balanced BreakHis dataset. The system utilises an efficient training methodology to learn the discerning features from images of different magnification levels. Data augmentation techniques are also employed to overcome the problem of overfitting. Additionally, the performance of CNN architecture has been improved in a significant manner by adopting an appropriate pooling strategy and optimisation technique. Based on that, we have achieved an accuracy of 80.76%, 76.58%, 79.90%, and 74.21% at the magnification 40X, 100X, 200X, and 400X, respectively. The proposed model outperforms the handcrafted approaches with an average accuracy of 80.47% at 40X magnification level.