ArticleComputational and mathematical methods in medicine2022
MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 8 citations in OpenAlex.
- Dual-channel compression mapping network with fused attention mechanism for medical image segmentation.Scientific reports · 2025Article
- Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN.Plants (Basel, Switzerland) · 2024Article
- Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network.BMC medical imaging · 2024Article
- SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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
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