A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi

• URL: http://arxiv.org/abs/2211.10972v1
• Date: Sun, 20 Nov 2022 12:55:42 GMT
• Title: A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi
• Authors: Sanya Sinha and Nilay Gupta
• Abstract summary: Unrepaired deoxyribo-nucleic acid (DNA) in skin cells, causes genetic defects in the skin and leads to skin cancer. Lesion-specific criteria are utilized to distinguish benign skin cancer from malignant melanoma. Five Transfer Learning-based techniques have the potential to be leveraged for the classification of melanocytic nevi.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skin cancer is a fatal manifestation of cancer. Unrepaired deoxyribo-nucleic acid (DNA) in skin cells, causes genetic defects in the skin and leads to skin cancer. To deal with lethal mortality rates coupled with skyrocketing costs of medical treatment, early diagnosis is mandatory. To tackle these challenges, researchers have developed a variety of rapid detection tools for skin cancer. Lesion-specific criteria are utilized to distinguish benign skin cancer from malignant melanoma. In this study, a comparative analysis has been performed on five Transfer Learning-based techniques that have the potential to be leveraged for the classification of melanocytic nevi. These techniques are based on deep convolutional neural networks (DCNNs) that have been pre-trained on thousands of open-source images and are used for day-to-day classification tasks in many instances.

Related papers

• Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques [39.97900702763419]
  This study focuses on analyzing dermoscopy images to determine the depth of melanomas. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. Results indicated that the models achieved significant improvements over previous methods.
  arXiv  Detail & Related papers  (2024-06-19T11:07:55Z)
• Skin Cancer Images Classification using Transfer Learning Techniques [0.0]
  Early diagnosis of skin cancer at a benign stage is critical to reducing cancer mortality. Previous studies have addressed the problem of skin cancer diagnosis using various deep learning and transfer learning models. In this work, we applied five different pre-trained transfer learning approaches for binary classification of skin cancer detection at benign and malignant stages.
  arXiv  Detail & Related papers  (2024-06-18T15:48:20Z)
• Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models [0.6827423171182154]
  Deep convolution neural networks (CNN) have shown an excellent potential for data and image classification. In this article, we inspect skin lesion classification problem using CNN techniques. We present that prominent classification accuracy of lesion detection can be obtained by proper designing and applying of transfer learning framework.
  arXiv  Detail & Related papers  (2024-04-01T15:06:20Z)
• 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)
• Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review [0.45545745874600063]
  "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features. Computer-aided diagnosis (CAD) has become a significant player in the research and development field. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi.
  arXiv  Detail & Related papers  (2023-09-01T05:50:47Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• Classification of Melanocytic Nevus Images using BigTransfer (BiT) [0.0]
  Melanocytic nevi may mature to cause fatal melanoma. Current management protocol involves the removal of those nevi that appear intimidating. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification.
  arXiv  Detail & Related papers  (2022-11-21T21:53:43Z)
• Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network [0.0]
  Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. It is difficult to detect and classify melanoma and nevus mole at the immature stages.
  arXiv  Detail & Related papers  (2022-09-30T13:35:24Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images [40.743870665742975]
  Existing dermatologists for automated melanoma diagnosis are based on single-time point images of lesions. We propose an automated framework for melanoma diagnosis using sequential dermoscopic images.
  arXiv  Detail & Related papers  (2020-06-19T04:08:22Z)

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.