Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This inclusion of automatic diagnostic systems speeds up diagnosis and helps medical professionals by relieving their work pressure. This study proposes a breast cancer detection framework based on a deep convolutional neural network. To mine useful information about breast cancer through breast histopathology images of the 40× magnification factor that are publicly available, the BreakHis dataset and IDC(Invasive ductal carcinoma) dataset are used. Pre-trained convolutional neural network (CNN) models EfficientNetB0, ResNet50, and Xception are tested for this study. The top layers of these architectures are replaced by custom layers to make the whole architecture specific to the breast cancer detection task. It is seen that the customized Xception model outperformed other frameworks. It gave an accuracy of 93.33% for the 40× zoom images of the BreakHis dataset. The networks are trained using 70% data consisting of BreakHis 40× histopathological images as training data and validated on 30% of the total 40× images as unseen testing and validation data. The histopathology image set is augmented by performing various image transforms. Dropout and batch normalization are used as regularization techniques. Further, the proposed model with enhanced pre-trained Xception CNN is fine-tuned and tested on a part of the IDC dataset. For the IDC dataset training, validation, and testing percentages are kept as 60%, 20%, and 20%, respectively. It obtained an accuracy of 88.08% for the IDC dataset for recognizing invasive ductal carcinoma from H&E-stained histopathological tissue samples of breast tissues. Weights learned during training on the BreakHis dataset are kept the same while training the model on IDC dataset. Thus, this study enhances and customizes functionality of pre-trained model as per the task of classification on the BreakHis and IDC datasets. This study also tries to apply the transfer learning approach for the designed model to another similar classification task.

With the advent of artificial intelligence, various computer-aided diagnostic systems are being developed to assist medical professionals. Deep learning techniques powered by convolutional neural networks seem promising for obtaining new insights into the onco-histopathology domain. Breast cancer is confirmed by histopathological analysis of Hematoxylin and Eosin (H&E)-stained breast tissue images. Finding the molecular subtype of breast cancer using Immunohistochemistry (IHC)-stained breast tissue is essential to decide on the correct treatment plan for a breast cancer patient. IHC staining is an expensive process that is very time-consuming and involves intra- and inter-observer subjectivity. This work attempts to find the Human Epidermal growth factor Receptor Two (HER2) molecular subtype from H&E-stained tissue images instead of using IHC-stained tissues. H&E-stained tissue image data from two separate sources are used to predict HER2 status. This study aimed to improve the accuracy of HER2 overexpression classification by modifying the architecture of the ResNet50 model by cascading it with a squeeze and excitation block and a depth-wise separable convolutional layer. The dataset comprises a combination of tissue image patches from a publicly available Warwick dataset and a real-world dataset collected from a hospital in Pune, India. The dataset is preprocessed and split into 60% train, 20% validation, and 20% test subsets. The proposed architecture with a modified ResNet50 network achieves the best patch-level HER2 classification accuracy of 98.04%, with class-specific test accuracy results for HER2 negative, HER2 equivocal, and HER2 positive scores being 97.73%, 99.70%, and 98.93%, respectively.