An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique

• URL: http://arxiv.org/abs/2410.14489v2
• Date: Tue, 29 Oct 2024 12:32:53 GMT
• Title: An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique
• Authors: Maksuda Akter, Rabea Khatun, Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin,
• Abstract summary: 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.
• Score: 2.621434923709917
• License:
• Abstract: Skin cancer is a serious and potentially fatal disease caused by DNA damage. Early detection significantly increases survival rates, making accurate diagnosis crucial. In this groundbreaking study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of benign and malignant skin lesions. Our approach begins with dataset preprocessing to enhance classification accuracy, followed by training two separate pre-trained DL models, InceptionV3 and DenseNet121. By fusing the results of each model using the weighted sum rule, our system achieves exceptional accuracy rates. Specifically, we achieve a 92.27% detection accuracy rate, 92.33% sensitivity, 92.22% specificity, 90.81% precision, and 91.57% F1-score, outperforming existing models and demonstrating the robustness and trustworthiness of our hybrid approach. Our study represents a significant advance in skin cancer diagnosis and provides a promising foundation for further research in the field. With the potential to save countless lives through earlier detection, our hybrid deep-learning approach is a game-changer in the fight against skin cancer.

Related papers

• A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification [1.024113475677323]
  Leukemia is diagnosed by analyzing blood and bone marrow smears under a microscope, with additional cytochemical tests for confirmation. Deep learning has provided advanced methods for classifying microscopic smear images, aiding in the detection of leukemic cells. In this research, hybrid deep learning models were implemented to classify Acute lymphoblastic leukemia (ALL) The proposed method achieved a remarkable detection accuracy rate of 100% on the ALL-IDB1 dataset, 98.07% on the ALL-IDB2 dataset, and 98.64% on the combined dataset.
  arXiv  Detail & Related papers  (2024-10-18T15:23:34Z)
• 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)
• Enhancing Skin Lesion Diagnosis with Ensemble Learning [15.569484546674776]
  This study examines the implementation of deep learning methods to assist in the diagnosis of skin lesions using the HAM10000 dataset. To further enhance classification accuracy, we developed ensemble models employing max voting, average voting, and stacking, resulting in accuracies of 0.803, 0.82, and 0.83. Building on the best-performing ensemble learning model, stacking, we developed our proposed model, SkinNet, which incorporates a customized architecture and fine-tuning, achieving an accuracy of 0.867 and an AUC of 0.96.
  arXiv  Detail & Related papers  (2024-09-06T16:19:01Z)
• Hybrid Deep Learning Framework for Enhanced Melanoma Detection [3.004788114489393]
  The primary objective of our study is to enhance the accuracy and efficiency of melanoma detection through an innovative hybrid approach. We utilize the HAM10000 dataset to meticulously train the U-Net model, enabling it to precisely segment cancerous regions. We employ the ISIC 2020 dataset to train the EfficientNet model, optimizing it for the binary classification of skin cancer.
  arXiv  Detail & Related papers  (2024-07-16T04:58:47Z)
• 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)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Visualizing CoAtNet Predictions for Aiding Melanoma Detection [0.0]
  This paper proposes a multi-class classification task using the CoAtNet architecture. It achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks.
  arXiv  Detail & Related papers  (2022-05-21T06:41:52Z)
• SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images [62.60956024215873]
  Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. This study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification.
  arXiv  Detail & Related papers  (2022-03-22T06:54:29Z)
• 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)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.