Cell nuclei classification in histopathological images using hybrid OLConvNet
This work addresses the challenge of automatic cancer diagnosis from histopathological images, offering a more flexible and interpretable method, though it appears incremental as it builds on existing deep learning techniques.
The authors tackled the problem of classifying cell nuclei in histopathological images for cancer detection by proposing a hybrid deep learning architecture called OLConvNet, which integrates object-level features with a shallower CNN to improve interpretability and reduce training time, achieving better performance than state-of-the-art models like AlexNet and ResNet50 as measured by F1-score and multiclass AUC.
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.