Classification of Skin Cancer Images using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2202.00678v1
• Date: Tue, 1 Feb 2022 17:11:41 GMT
• Title: Classification of Skin Cancer Images using Convolutional Neural Networks
• Authors: Kartikeya Agarwal, Tismeet Singh
• Abstract summary: Skin cancer is the most common human malignancy. Deep neural networks show humongous potential for image classification. Highest model accuracy achieved was over 86.65%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Skin cancer is the most common human malignancy(American Cancer Society) which is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic(related to skin) analysis, a biopsy and histopathological examination. Skin cancer occurs when errors (mutations) occur in the DNA of skin cells. The mutations cause the cells to grow out of control and form a mass of cancer cells. The aim of this study was to try to classify images of skin lesions with the help of convolutional neural networks. The deep neural networks show humongous potential for image classification while taking into account the large variability exhibited by the environment. Here we trained images based on the pixel values and classified them on the basis of disease labels. The dataset was acquired from an Open Source Kaggle Repository(Kaggle Dataset)which itself was acquired from ISIC(International Skin Imaging Collaboration) Archive. The training was performed on multiple models accompanied with Transfer Learning. The highest model accuracy achieved was over 86.65%. The dataset used is publicly available to ensure credibility and reproducibility of the aforementioned result.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• 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)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets [4.83420384410068]
  We evaluate Siamese Neural Networks (SNNs) on both dermoscopic and clinical images of skin lesions. SNNs allow us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on clinical and dermoscopic datasets.
  arXiv  Detail & Related papers  (2022-12-12T18:58:43Z)
• 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)
• Analysis of skin lesion images with deep learning [0.0]
  We evaluate the current state of the art in the classification of dermoscopic images. Various deep neural network architectures pre-trained on the ImageNet data set are adapted to a combined training data set. The performance and applicability of these models for the detection of eight classes of skin lesions are examined.
  arXiv  Detail & Related papers  (2021-01-11T10:58:36Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• Method to Classify Skin Lesions using Dermoscopic images [0.0]
  Skin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. In this project, an automated model for skin lesion classification using dermoscopic images has been developed with CNN(Convolution Neural Networks) as a training model. The best accuracy this model could achieve is 0.886.
  arXiv  Detail & Related papers  (2020-08-21T10:58:33Z)
• Detector-SegMentor Network for Skin Lesion Localization and Segmentation [0.0]
  Melanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages. We propose a network-in-network convolution neural network based approach for segmentation of the skin lesion.
  arXiv  Detail & Related papers  (2020-05-13T19:41:27Z)
• An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization [45.00998416720726]
  We propose a framework to address the unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a final prediction.
  arXiv  Detail & Related papers  (2020-02-13T15:28:42Z)

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.