MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$-Means Clustering

Abstract

Melanoma is a dangerous form of skin cancer that results in the demise of patients at the developed stage. Researchers have attempted to develop automated systems for the timely recognition of this deadly disease. However, reliable and precise identification of melanoma moles is a tedious and complex activity as there exist huge differences in the mass, structure, and color of the skin lesions. Additionally, the incidence of noise, blurring, and chrominance changes in the suspected images further enhance the complexity of the detection procedure. In the proposed work, we try to overcome the limitations of the existing work by presenting a deep learning (DL) model. Descriptively, after accomplishing the preprocessing task, we have utilized an object detection approach named CornerNet model to detect melanoma lesions. Then the localized moles are passed as input to the fuzzy K -means (FLM) clustering approach to perform the segmentation task. To assess the segmentation power of the proposed approach, two standard databases named ISIC-2017 and ISIC-2018 are employed. Extensive experimentation has been conducted to demonstrate the robustness of the proposed approach through both numeric and pictorial results. The proposed approach is capable of detecting and segmenting the moles of arbitrary shapes and orientations. Furthermore, the presented work can tackle the presence of noise, blurring, and brightness variations as well. We have attained the segmentation accuracy values of 99.32% and 99.63% over the ISIC-2017 and ISIC-2018 databases correspondingly which clearly depicts the effectiveness of our model for the melanoma mole segmentation.

Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network

“…The end-to-end approach treats segmentation and identification as inherently connected methods, where a high Jaccard index also indicates the stability of melanoma identification. Nawaz M et al [7] present a deep learning approach to overcome the limitations of previous work. After completing the pre-processing procedure, they utilize the Corner-Net framework, an image detection technique, to diagnose melanoma lesions.…”

“…The presence of noise or blurring, along with luminance modifications in suspicious images, contributes to making detection more complex. To overcome the limitations of previous research, we demonstrate a DL model in this paper [7]. Excessive sunlight exposure can often be attributed as the cause of melanoma, with digital dermoscopy serving as a tool for identifying cancerous areas within skin lesions.…”

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

“…In order to evaluate the impact of YOLOv8-ASFF on detecting tea leaf blight, tea white spot disease, and tea sooty disease in Yunnan large-leaf tea, four sets of comparative experiments were conducted. The experiments compared YOLOv8-ASFF with four established mainstream network models, including YOLOv8 [35], YOLOv5 [36], CornerNet [37], and SSD [38]. To ensure the reliability of the model test results, the hardware equipment and software environment were kept consistent throughout the study.…”