  Computer-aided histopathological image analysis for cancer detection is a
major research challenge in the medical domain. Automatic detection and
classification of nuclei for cancer diagnosis impose a lot of challenges in
developing state of the art algorithms due to the heterogeneity of cell nuclei
and data set variability. Recently, a multitude of classification algorithms
has used complex deep learning models for their dataset. However, most of these
methods are rigid and their architectural arrangement suffers from
inflexibility and non-interpretability. In this research article, we have
proposed a hybrid and flexible deep learning architecture OLConvNet that
integrates the interpretability of traditional object-level features and
generalization of deep learning features by using a shallower Convolutional
Neural Network (CNN) named as $CNN_{3L}$. $CNN_{3L}$ reduces the training time
by training fewer parameters and hence eliminating space constraints imposed by
deeper algorithms. We used F1-score and multiclass Area Under the Curve (AUC)
performance parameters to compare the results. To further strengthen the
viability of our architectural approach, we tested our proposed methodology
with state of the art deep learning architectures AlexNet, VGG16, VGG19,
ResNet50, InceptionV3, and DenseNet121 as backbone networks. After a
comprehensive analysis of classification results from all four architectures,
we observed that our proposed model works well and perform better than
contemporary complex algorithms.
