ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging

• URL: http://arxiv.org/abs/2403.14248v1
• Date: Thu, 21 Mar 2024 09:07:28 GMT
• Title: ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging
• Authors: Sibasish Dhibar,
• Abstract summary: We introduce an innovative convolutional ensemble network approach named deep autoencoder (DAE) with ResNet101. This method utilizes convolution-based deep neural networks for the detection of skin cancer. The methods result in 96.03% of accuracy, 95.40 % of precision, 96.05% of recall, 0.9576 of F-measure, 0.98 of AUC.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skin cancer is a crucial health issue that requires timely detection for higher survival rates. Traditional computer vision techniques face challenges in addressing the advanced variability of skin lesion features, a gap partially bridged by convolutional neural networks (CNNs). To overcome the existing issues, we introduce an innovative convolutional ensemble network approach named deep autoencoder (DAE) with ResNet101. This method utilizes convolution-based deep neural networks for the detection of skin cancer. The ISIC-2018 public data taken from the source is used for experimental results, which demonstrate remarkable performance with the different in terms of performance metrics. The methods result in 96.03% of accuracy, 95.40 % of precision, 96.05% of recall, 0.9576 of F-measure, 0.98 of AUC.

Related papers

• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images [2.833640239679924]
  Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge. We propose a hybrid deep learning-based architecture that uses transfer learning to extract the features from pre-trained convolutional neural network (CNN) models. The proposed hybrid model showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases.
  arXiv  Detail & Related papers  (2024-12-04T07:26:36Z)
• Advanced Hybrid Deep Learning Model for Enhanced Classification of Osteosarcoma Histopathology Images [0.0]
  This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. We propose a novel hybrid model that combines convolutional neural networks (CNN) and vision transformers (ViT) to improve diagnostic accuracy for OS. The model achieved an accuracy of 99.08%, precision of 99.10%, recall of 99.28%, and an F1-score of 99.23%.
  arXiv  Detail & Related papers  (2024-10-29T13:54:08Z)
• An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique [2.621434923709917]
  Skin cancer is a serious and potentially fatal disease caused by DNA damage. In this study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of skin lesions. We achieve a 92.27% detection accuracy rate, 92.33% sensitivity, 92.22% specificity, 90.81% precision, and 91.57% F1-score.
  arXiv  Detail & Related papers  (2024-10-18T14:19:13Z)
• Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification [0.0]
  We propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification. We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework. We also introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net)
  arXiv  Detail & Related papers  (2024-07-03T06:40:26Z)
• Breast Cancer Image Classification Method Based on Deep Transfer Learning [40.392772795903795]
  A breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0% in the test set, with a significantly improved classification accuracy compared to previous models.
  arXiv  Detail & Related papers  (2024-04-14T12:09:47Z)
• Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks [0.0]
  This study introduces an innovative approach by integrating Graph Neural Networks (GNNs) with Capsule Networks to enhance classification performance. Our research focuses on evaluating and enhancing the Tiny Pyramid Vision GNN (Tiny Pyramid ViG) architecture by incorporating it with a Capsule Network. After 75 epochs of training, our model achieved a significant accuracy improvement, reaching 89.23% and 95.52%, surpassing established benchmarks.
  arXiv  Detail & Related papers  (2024-03-18T17:47:39Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• 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)
• 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)
• COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images [34.93885932923011]
  Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. There has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs) The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets.
  arXiv  Detail & Related papers  (2020-04-06T14:20:47Z)

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.