Abstract
Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a Ground-Truth Irregular Pigment Network Dataset. We Trained Multiple Transfer Learned Segmentation Models to Detect Irregular Networks in This Training Set. a Separate, Mutually Exclusive Subset of the International Skin Imaging Collaboration (ISIC) 2019 Dataset with 500 Melanomas and 500 Benign Lesions Was Used for Training and Testing Deep Learning Models for the Binary Classification of Melanoma Versus Benign. the Best Segmentation Model, U-Net++, Generated Irregular Network Masks on the 1000-Image Dataset. Other Classical Color, Texture, and Shape Features Were Calculated for the Irregular Network Areas. We Achieved an Increase in the Recall of Melanoma Versus Benign of 11% and in Accuracy of 2% over DL-Only Models using Conventional Classifiers in a Sequential Pipeline based on the Cascade Generalization Framework, with the Highest Increase in Recall Accompanying the Use of the Random Forest Algorithm. the Proposed Approach Facilitates Leveraging the Strengths of Both Deep Learning and Conventional Image Processing Techniques to Improve the Accuracy of Melanoma Diagnosis. Further Research Combining Deep Learning with Conventional Image Processing on Automatically Detected Dermoscopic Features is Warranted.
Recommended Citation
A. K. Nambisan and A. Maurya and N. Lama and T. Phan and G. Patel and K. Miller and B. Lama and J. Hagerty and R. Stanley and W. V. Stoecker, "Improving Automatic Melanoma Diagnosis using Deep Learning-Based Segmentation of Irregular Networks," Cancers, vol. 15, no. 4, article no. 1259, MDPI, Feb 2023.
The definitive version is available at https://doi.org/10.3390/cancers15041259
Department(s)
Chemistry
Keywords and Phrases
Angulated Lines; Atypical Network; Branch Streaks; Cascade Generalization; Deep Learning; Fusion; Machine Learning; Melanoma
International Standard Serial Number (ISSN)
2072-6694
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Feb 2023
