Histopathology images are an essential resource for defining biological compositions or examining the composition of cells and tissues. The analysis of histopathology images is also crucial in supporting different class of disease including for rare disease like Myeloproliferative Neoplasms (MPN). Despite technological advancement in diagnostic tools to boost procedure in classification of MPN, morphological assessment from histopathology images acquired by bone marrow trephine (BMT) is remained critical to confirm MPN subtypes. However, the outcome of assessment at a present is profoundly challenging due to subjective, poorly reproducible criteria and highly dependent on pathologist where it caused interobserver variability in the interpretation. To address, this study developed a classification of classical MPN namely polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (MF) using deep learning approach. Data collection was undergoing several image augmentations processes to increase features variability and expand the dataset. The augmented images were then fed into CNN classifier followed by implementation of cross validation method. Finally, the best classification model was performed 95.3% of accuracy by using Adamax optimizer. High accuracy and best output given by proposed model shows significant potential in the deployment of the classification of MPN and hence facilitates the interpretation and monitoring of samples beyond conventional approaches.
Many diseases require histopathology images to characterise biological components or study cell and tissue architectures. The histopathology images are also essential in supporting disease classification, including myeloproliferative neoplasms (MPN). Despite significant developments to improve the diagnostic tools, morphological assessment from histopathology images obtained by bone marrow trephine (BMT) remains crucial to confirm MPN subtypes. However, the assessment outcome is challenging due to subjective characteristics that are hard to replicate due to its inter-observer variability. Apart from that, image processing may reduce the quality of the BMT images and affect the diagnosis result. This study has developed a classification system for classical MPN subtypes: polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (MF). It was done by reconstructing low-resolution images of BMT using a super-resolution approach to address the issue. Identified low-resolution images from calculating Laplacian variance were reconstructed using a super-resolution convolution neural network (SRCNN) to transform into rich information of high-resolution images. Original BMT images and reconstructed BMT images using the SRCNN dataset were fed into a CNN classifier, and the classifier’s output for both datasets was compared accordingly. Based on the result, the dataset consisting of the reconstructed images showed better output with 92% accuracy, while the control images gave 88% accuracy. In conclusion, the high quality of histopathology images substantially impacts disease process classification, and the reconstruction of low-resolution images has improved the classification output.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.