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Computer-aided histopathological image analysis for cancer detection is amajor research challenge in the medical domain. Automatic detection andclassification of nuclei for cancer diagnosis impose a lot of challenges indeveloping state of the art algorithms due to the heterogeneity of cell nucleiand data set variability. Recently, a multitude of classification algorithmshas used complex deep learning models for their dataset. However, most of thesemethods are rigid and their architectural arrangement suffers frominflexibility and non-interpretability. In this research article, we haveproposed a hybrid and flexible deep learning architecture OLConvNet thatintegrates the interpretability of traditional object-level features andgeneralization of deep learning features by using a shallower ConvolutionalNeural Network (CNN) named as $CNN_{3L}$. $CNN_{3L}$ reduces the training timeby training fewer parameters and hence eliminating space constraints imposed bydeeper algorithms. We used F1-score and multiclass Area Under the Curve (AUC)performance parameters to compare the results. To further strengthen theviability of our architectural approach, we tested our proposed methodologywith state of the art deep learning architectures AlexNet, VGG16, VGG19,ResNet50, InceptionV3, and DenseNet121 as backbone networks. After acomprehensive analysis of classification results from all four architectures,we observed that our proposed model works well and perform better thancontemporary complex algorithms.

Cell nuclei classification in histopathological images using hybrid OLConvNet