Enhancing Skin Lesion Diagnosis with Ensemble Learning

• URL: http://arxiv.org/abs/2409.04381v1
• Date: Fri, 6 Sep 2024 16:19:01 GMT
• Title: Enhancing Skin Lesion Diagnosis with Ensemble Learning
• Authors: Xiaoyi Liu, Zhou Yu, Lianghao Tan, Yafeng Yan, Ge Shi,
• Abstract summary: 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.
• Score: 15.569484546674776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skin lesions are an increasingly significant medical concern, varying widely in severity from benign to cancerous. Accurate diagnosis is essential for ensuring timely and appropriate treatment. This study examines the implementation of deep learning methods to assist in the diagnosis of skin lesions using the HAM10000 dataset, which contains seven distinct types of lesions. First, we evaluated three pre-trained models: MobileNetV2, ResNet18, and VGG11, achieving accuracies of 0.798, 0.802, and 0.805, respectively. 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. This substantial improvement over individual models demonstrates the effectiveness of ensemble learning in improving skin lesion classification.

Related papers

• A Foundational Generative Model for Breast Ultrasound Image Analysis [42.618964727896156]
  Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. We present BUSGen, the first foundational generative model specifically designed for breast ultrasound analysis. With few-shot adaptation, BUSGen can generate repositories of realistic and informative task-specific data.
  arXiv  Detail & Related papers  (2025-01-12T16:39:13Z)
• An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types [0.9467360130705921]
  This study advances the field by curating a comprehensive and diverse dataset comprising 39 categories of skin lesions. Five state-of-the-art deep learning models -- MobileNetV2, Xception, InceptionV3, EfficientNetB1, and Vision Transformer - are rigorously evaluated. The Vision Transformer model integrated with CBAM outperforms others, achieving an accuracy of 93.46%, precision of 94%, recall of 93%, F1-score of 93%, and specificity of 93.67%.
  arXiv  Detail & Related papers  (2025-01-10T14:25:01Z)
• KneeXNeT: An Ensemble-Based Approach for Knee Radiographic Evaluation [5.715859759904031]
  Knee osteoarthritis (OA) is the most common joint disorder and a leading cause of disability. This study developed an automated deep learning model to classify knee OA severity, reducing the need for expert evaluation. Our ensemble model, KneeXNet, achieved the highest accuracy of 0.72, demonstrating its potential as an automated tool for knee OA assessment.
  arXiv  Detail & Related papers  (2024-12-10T14:02:04Z)
• 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)
• 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)
• Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques [0.0]
  Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. We implemented six machine learning models using the MIMIC-III database.
  arXiv  Detail & Related papers  (2024-08-02T09:44:18Z)
• Enhancing Skin Disease Classification Leveraging Transformer-based Deep Learning Architectures and Explainable AI [2.3149142745203326]
  Skin diseases affect over a third of the global population, yet their impact is often underestimated. Deep learning techniques have shown much promise for various tasks, including dermatological disease identification. This study uses a skin disease dataset with 31 classes and compares it with all versions of Vision Transformers, Swin Transformers and DivoV2.
  arXiv  Detail & Related papers  (2024-07-20T05:38:00Z)
• 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)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability [54.86482395312936]
  Deep learning (DL) classification models were trained to identify COVID-19-positive patients on 3D computed tomography (CT) datasets from different countries. We trained nine identical DL-based classification models by using combinations of the datasets with a 72% train, 8% validation, and 20% test data split. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better.
  arXiv  Detail & Related papers  (2021-02-18T21:14:52Z)

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.