Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification

• URL: http://arxiv.org/abs/2006.14715v1
• Date: Thu, 25 Jun 2020 21:51:24 GMT
• Title: Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification
• Authors: Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Georg Dorffner, Isabella Ellinger
• Abstract summary: Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels support good performance with larger image sizes leading to slightly improved classification.
• Score: 3.110738188734789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of great interest. Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. Pre-trained CNNs are usually trained with natural images of a fixed image size which is typically significantly smaller than captured skin lesion images and consequently dermoscopic images are downsampled for fine-tuning. However, useful medical information may be lost during this transformation. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. For this, we resize dermoscopic images to different resolutions, ranging from 64x64 to 768x768 pixels and investigate the resulting classification performance of three well-established CNNs, namely DenseNet-121, ResNet-18, and ResNet-50. Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification. We further propose a novel fusion approach based on a three-level ensemble strategy that exploits multiple fine-tuned networks trained with dermoscopic images at various sizes. When applied on the ISIC 2017 skin lesion classification challenge, our fusion approach yields an area under the receiver operating characteristic curve of 89.2% and 96.6% for melanoma classification and seborrheic keratosis classification, respectively, outperforming state-of-the-art algorithms.

Related papers

• Skin Lesion Diagnosis Using Convolutional Neural Networks [0.30458514384586394]
  This project aims to address the issue by collecting state-of-the-art techniques for image classification from various fields. The models were trained using a dataset of 8012 images, and their performance was evaluated using 2003 images. It's worth noting that this model is trained end-to-end, directly from the image to the labels, without the need for handcrafted feature extraction.
  arXiv  Detail & Related papers  (2023-05-18T17:15:08Z)
• 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)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• DenseNet approach to segmentation and classification of dermatoscopic skin lesions images [0.0]
  This paper proposes an improved method for segmentation and classification for skin lesions using two architectures. The combination of U-Net and DenseNet121 provides acceptable results in dermatoscopic image analysis. cancerous and non-cancerous samples were detected in DenseNet121 network with 79.49% and 93.11% accuracy respectively.
  arXiv  Detail & Related papers  (2021-10-09T19:12:23Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• 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)
• Metastatic Cancer Image Classification Based On Deep Learning Method [7.832709940526033]
  We propose a noval method which combines the deep learning algorithm in image classification, the DenseNet169 framework and Rectified Adam optimization algorithm. Our model achieves superior performance over the other classical convolutional neural networks approaches, such as Vgg19, Resnet34, Resnet50.
  arXiv  Detail & Related papers  (2020-11-13T16:04:39Z)
• A Deep Learning Study on Osteosarcoma Detection from Histological Images [6.341765152919201]
  The most common type of primary malignant bone tumor is osteosarcoma. CNNs can significantly decrease surgeon's workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance.
  arXiv  Detail & Related papers  (2020-11-02T18:16:17Z)
• Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma [7.919213739992465]
  The architecture is based on convolu-tional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used. The best performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%.
  arXiv  Detail & Related papers  (2020-07-10T15:39:32Z)
• Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations [70.0118756144807]
  This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms. A modified Y-Net architecture based on the VGG11 encoder is used to simultaneously learn geometric orientation and segmentation of radiographs. Results were evaluated by expert clinicians, with acceptable geometry in 95.8% and annotation mask in 96.2%, compared to 27.0% and 34.9% respectively in control images.
  arXiv  Detail & Related papers  (2020-05-08T02:16:17Z)

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.