Physical Sciences and Mathematics Commons^™

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation Of Irregular Networks, Anand K. Nambisan, Akanksha Maurya, Norsang Lama, Thanh Phan, Gehana Patel, Keith Miller, Binita Lama, Jason Hagerty, Ronald Stanley, William V. Stoecker

Chemistry Faculty Research & Creative Works

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 …

Analysis Of Globule Types In Malignant Melanoma, Jin Xu, Kapil Kumar Gupta, William V. Stoecker, Yamini Krishnamurthy, Harold S. Rabinovitz, Austin Bangert, David A. Calcara, Margaret C. Oliviero, Joseph M. Malters, Rhett J. Drugge, R. Joe Stanley, Randy Hays Moss, Mehmed Emre Celebi

Chemistry Faculty Research & Creative Works

Objective: To identify and analyze subtypes of globules based on size, shape, network connectedness, pigmentation, and distribution to determine which globule types and globule distributions are most frequently associated with a diagnosis of malignant melanoma. Design: Retrospective case series of dermoscopy images with globules. Setting: Private dermatology practices. Participants: Patients in dermatology practices. Intervention: Observation only. Main Outcome Measure: Association of globule types with malignant melanoma. Results: The presence of large globules (odds ratio [OR], 5.25) and globules varying in size (4.72) or shape (5.37) had the highest ORs for malignant melanoma among all globule types and combinations studied. Classical …

Melanoma And Seborrheic Keratosis Differentiation Using Texture Features, Srinivas V. Deshabhoina, Scott E. Umbaugh, William V. Stoecker, Randy Hays Moss, Subhashini K. Srinivasan

Chemistry Faculty Research & Creative Works

Purpose: To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.

Methods: A systematic approach to consistent classification of skin tumors is described. Texture features, based on the second-order histogram, were used to identify the features or a combination of features that could consistently differentiate a malignant skin tumor (melanoma) from a benign one (seborrheic keratosis). Two hundred and seventy-one skin tumor images were separated into training and test sets for accuracy and consistency. Automatic induction was applied to generate classification rules. Data analysis and modeling tools were used to gain further insight into the feature space. …

Detection Of Solid Pigment In Dermatoscopy Images Using Texture Analysis, Murali Anantha, William V. Stoecker, Randy Hays Moss

Chemistry Faculty Research & Creative Works

Background/aims: Epiluminescence microscopy (ELM), also known as dermoscopy or dermatoscopy, is a non-invasive, in vivo technique, that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such feature is the solid pigment, also called the blotchy pigment or dark structureless area. Our goal was to automatically detect this feature and determine whether its presence is useful in distinguishing benign from malignant pigmented lesions.

Methods: Here, a texture-based algorithm is developed for the detection of solid pigment. The factors d and a …