Optimizing Neuro-Fuzzy and Colonial Competition Algorithms for Skin Cancer Diagnosis in Dermatoscopic Images

• URL: http://arxiv.org/abs/2505.08886v1
• Date: Tue, 13 May 2025 18:17:19 GMT
• Title: Optimizing Neuro-Fuzzy and Colonial Competition Algorithms for Skin Cancer Diagnosis in Dermatoscopic Images
• Authors: Hamideh Khaleghpour, Brett McKinney,
• Abstract summary: This study aims to bridge the gap by employing a fusion of image processing techniques and machine learning algorithms.<n>Applying to dermoscopic images from the ISIC database, our method achieved a notable accuracy of 94%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for advanced diagnostic aids. Artificial Intelligence (AI) has emerged as a promising tool in this domain, particularly for distinguishing malignant from benign skin lesions. Leveraging publicly available datasets of skin lesions, researchers have been developing AI-based diagnostic solutions. However, the integration of such computer systems in clinical settings is still nascent. This study aims to bridge this gap by employing a fusion of image processing techniques and machine learning algorithms, specifically neuro-fuzzy and colonial competition approaches. Applied to dermoscopic images from the ISIC database, our method achieved a notable accuracy of 94% on a dataset of 560 images. These results underscore the potential of our approach in aiding clinicians in the early detection of melanoma, thereby contributing significantly to skin cancer diagnostics.

Related papers

• AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications [0.3937575566991286]
  This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers.<n>The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis.<n>The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures.
  arXiv  Detail & Related papers  (2025-01-26T11:32:43Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered.<n>This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images.<n>The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma [10.403042621991611]
  Classifying carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a critical step in treatment selection and prognosis evaluation. In this study, we introduce a novel polarization and radiomics feature fusion network, which combines polarization features with radiomics features to classify HCC and ICC.
  arXiv  Detail & Related papers  (2023-12-27T15:16:04Z)
• Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images [61.36288157482697]
  Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images.
  arXiv  Detail & Related papers  (2023-11-20T10:45:39Z)
• Case Studies on X-Ray Imaging, MRI and Nuclear Imaging [0.0]
  We will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images.
  arXiv  Detail & Related papers  (2023-06-03T09:05:35Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Towards Realization of Augmented Intelligence in Dermatology: Advances and Future Directions [0.0]
  Artificial intelligence (AI) algorithms using deep learning have advanced the classification of skin disease images. These algorithms have been mostly applied "in silico" and not validated clinically.
  arXiv  Detail & Related papers  (2021-05-21T17:39:16Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers [0.6445605125467573]
  Automatic Lesion Detection System (ALDS) helps physicians and dermatologists to obtain a second opinion for proper analysis and treatment of skin cancer. This paper is focused towards the development of improved ALDS framework based on probabilistic approach.
  arXiv  Detail & Related papers  (2020-03-13T13:31:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.