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Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
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Research article

Super-Resolution Approach to Enhance Bone Marrow Trephine Image in the Classification of Classical Myeloproliferative Neoplasms

Umi Kalsom Mohamad Yusof, Syamsiah Mashohor, Marsyita Hanafi, Sabariah Md Noor and Norsafina Zainal

Volume 31, Issue 5 August 2023 Published 31 July 2023
https://doi.org/10.47836/pjst.31.5.02
KeywordsArtificial intelligence, histopathology images, image reconstruction, myeloproliferative neoplasm, super-resolution
Article content

Abstract

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.
Supporting literature

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