An efficient CNN based algorithm for detecting melanoma cancer regions in H&E-stained images

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 3982 - 3985
• Main Authors: Alheejawi, Salah, Berendt, Richard, Jha, Naresh, Maity, Santi P., Mandal, Mrinal
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Algorithms; Computer architecture; Deep learning; Eosine Yellowish-(YS); Hematoxylin; Histopathological image analysis; Humans; Image segmentation; Melanoma; Melanoma - diagnostic imaging; Melanoma detection; Microprocessors; Neural networks; Nuclei segmentation; Skin; Skin Neoplasms - diagnosis
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9630443

Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.