Physical Sciences and Mathematics Commons^™

Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such …

Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes …

Real-Time Supervised Detection Of Pink Areas In Dermoscopic Images Of Melanoma: Importance Of Color Shades, Texture And Location, Ravneet Kaur, P. P. Albano, Justin G. Cole, Jason R. Hagerty, Robert W. Leander, Randy Hays Moss, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Background/Purpose: Early detection of malignant melanoma is an important public health challenge. In the USA, dermatologists are seeing more melanomas at an early stage, before classic melanoma features have become apparent. Pink color is a feature of these early melanomas. If rapid and accurate automatic detection of pink color in these melanomas could be accomplished, there could be significant public health benefits.

Methods: Detection of three shades of pink (light pink, dark pink, and orange pink) was accomplished using color analysis techniques in five color planes (red, green, blue, hue, and saturation). Color shade analysis was performed using a logistic …

Microwave Reflectometry As A Novel Diagnostic Tool For Detection Of Skin Cancers, Pratik Mehta, Kundan Chand, Deepak Narayanswamy, Daryl G. Beetner, R. Zoughi, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

More than 1 000 000 people are diagnosed with skin cancer each year in the United States, and more than 10 000 people die from the disease. Methods such as visual inspection and dermoscopy are available for early detection of skin cancers, but improvement in accuracy is needed. This paper investigates the use of microwave reflectometry as a potential diagnostic tool for detection of skin cancers. Open-ended coaxial probes were used to measure microwave properties of skin. The influences of measurement parameters such as probe application pressure, power level, and variation in reflection properties of skin with location and hydration …