  Cancers are the leading cause of death in many countries. Early diagnosis
plays a crucial role in having proper treatment for this debilitating disease.
The automated classification of the type of cancer is a challenging task since
pathologists must examine a huge number of histopathological images to detect
infinitesimal abnormalities. In this study, we propose a novel convolutional
neural network (CNN) architecture composed of a Concatenation of multiple
Networks, called C-Net, to classify biomedical images. The model incorporates
multiple CNNs including Outer, Middle, and Inner. The first two parts of the
architecture contain six networks that serve as feature extractors to feed into
the Inner network to classify the images in terms of malignancy and benignancy.
The C-Net is applied for histopathological image classification on two public
datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the
model is tested using several evaluation metrics for its reliability. The C-Net
model outperforms all other models on the individual metrics for both datasets
and achieves zero misclassification. This approach has the potential to be
extended to additional classification tasks, as experimental results
demonstrate utilizing extensive evaluation metrics.
