A Comprehensive Evaluation Study on Risk Level Classification of
Melanoma by Computer Vision on ISIC 2016-2020 Datasets
- URL: http://arxiv.org/abs/2302.09528v1
- Date: Sun, 19 Feb 2023 09:58:58 GMT
- Title: A Comprehensive Evaluation Study on Risk Level Classification of
Melanoma by Computer Vision on ISIC 2016-2020 Datasets
- Authors: Chengdong Yao
- Abstract summary: Melanoma is the cause of 75% of skin cancer deaths.
Better detection of melanoma could have a positive impact on millions of people.
ISIC archive contains the largest publicly available collection of dermatoscopic images of skin lesions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Skin cancer is the most common type of cancer. Specifically, melanoma is the
cause of 75% of skin cancer deaths, although it is the least common skin
cancer. Better detection of melanoma could have a positive impact on millions
of people. The ISIC archive contains the largest publicly available collection
of dermatoscopic images of skin lesions. In this research, we investigate the
efficacy of applying advanced deep learning techniques in computer vision to
identify melanoma in images of skin lesions. Through reviewing previous
methods, including pre-trained models, deep-learning classifiers, transfer
learning, etc., we demonstrate the applicability of the popular deep learning
methods on critical clinical problems such as identifying melanoma. Finally, we
proposed a processing flow with a validation AUC greater than 94% and a
sensitivity greater than 90% on ISIC 2016 - 2020 datasets.
Related papers
- Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer [0.0]
We employ a Vision Transformer (ViT) that has been developed based on the idea of a self-attention mechanism.
The ViT-L32 model achieves an accuracy of 91.57% and a melanoma recall of 58.54%, while ViT-L16 achieves an accuracy of 92.79% and a melanoma recall of 56.10%.
arXiv Detail & Related papers (2024-07-26T07:06:42Z) - Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
Grading plays a vital role in breast cancer treatment planning.
The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs.
This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
arXiv Detail & Related papers (2024-05-13T15:48:26Z) - Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer.
The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts.
This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
arXiv Detail & Related papers (2024-05-13T15:40:56Z) - 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) - Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep
Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023.
The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy.
In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
arXiv Detail & Related papers (2023-04-12T15:08:34Z) - A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer
Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data [82.74877848011798]
Cancer-Net BCa is a multi-institutional open-source benchmark dataset of volumetric CDI$s$ imaging data of breast cancer patients.
Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
arXiv Detail & Related papers (2023-04-12T05:41:44Z) - A Comparative Analysis of Transfer Learning-based Techniques for the
Classification of Melanocytic Nevi [0.0]
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.
arXiv Detail & Related papers (2022-11-20T12:55:42Z) - 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) - A Smartphone based Application for Skin Cancer Classification Using Deep
Learning with Clinical Images and Lesion Information [1.8199326045904993]
Deep neural networks (DNNs) have become viable to deal with skin cancer detection.
In this work, we present a smartphone-based application to assist on skin cancer detection.
arXiv Detail & Related papers (2021-04-28T16:51:00Z) - CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of
Skin Cancer from Dermoscopy Images [71.68436132514542]
Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S.
In this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images.
arXiv Detail & Related papers (2020-11-21T02:17:59Z) - AI outperformed every dermatologist: Improved dermoscopic melanoma
diagnosis through customizing batch logic and loss function in an optimized
Deep CNN architecture [2.572959153453185]
This study proposes a method using deep convolutional neural networks aiming to detect melanoma as a binary classification problem.
It involves 3 key features, namely customized batch logic, customized loss function and reformed fully connected layers.
The model outperformed all 157 dermatologists and achieved state-of-the-art performance with AUC at 94.4% with sensitivity of 85.0% and specificity of 95.0%.
arXiv Detail & Related papers (2020-03-05T13:19:13Z)
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.