A color-based deep-learning approach for tissue slide lung cancer classification

Non-Small Cell Lung Cancer is the most common type of lung cancer, accounting for more than 80% of all cases. The analysis of histopathological images is the appropriate way to detect lung cancer in its initial stages. However, due to the heterogeneous patterns of molecular structures and the high magnification factor, the classification of whole slide images is difficult. Furthermore, these large-pixel images cannot be considered directly due to the high computational requirements. Several deep-learning models have been developed for the classification of histopathological images, but only in the default RGB format. Existing models are complex and rely on pathologist annotations which increases the computational cost and time. The present paper proposes a deep-learning framework, a Color-based Dilated Convolutional Neural Network (CD-CNN) for the multi-class classification of patch-based whole slide images of lung cancer. The proposed CD-CNN framework includes data preparation, color space transformation, and the classification stages. It thoroughly examines the impact of five color spaces on classification performance. The proposed model was also compared to three pre-trained models and the Convolutional Neural Network model on three different datasets namely: The Cancer Genome Atlas dataset, the Clinical Proteomic Tumor Analysis Consortium cohorts, and the LC25000 dataset. The analysis of all the models reveals that the HSV color space outperforms the other transformations for all of the datasets tested. The results depict that the proposed CD-CNN achieved the best classification results in HSV color space for all three datasets i.e. accuracy of 0.97-0.99, precision, recall, and F1 score 0.97-0.98 and AUC value of 0.970-0.984. In addition, the highest value of the kappa score 0.986 has been achieved by the proposed model. These findings demonstrate the efficacy of the proposed method and show an improvement in diagnostic systems.