Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation

• URL: http://arxiv.org/abs/2511.21582v2
• Date: Sat, 29 Nov 2025 14:26:42 GMT
• Title: Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation
• Abstract summary: This research intends to use deep learning to build a multiclass classifier for sixteen different oral lesions.<n>The experimental results, which achieved 83.33 percent accuracy, 89.12 percent precision, and 77.31 percent recall, demonstrate the superiority of the suggested model.
• Score: 0.25489046505746704
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Oral cancer is highly common across the globe and is mostly diagnosed during the later stages due to the close visual similarity to benign, precancerous, and malignant lesions in the oral cavity. Implementing computer aided diagnosis systems early on has the potential to greatly improve clinical outcomes. This research intends to use deep learning to build a multiclass classifier for sixteen different oral lesions. To overcome the challenges of limited and imbalanced datasets, the proposed technique combines stratified data splitting and advanced data augmentation and oversampling to perform the classification. The experimental results, which achieved 83.33 percent accuracy, 89.12 percent precision, and 77.31 percent recall, demonstrate the superiority of the suggested model over state of the art methods now in use. The suggested model effectively conveys the effectiveness of oversampling and augmentation strategies in situations where the minority class classification performance is noteworthy. As a first step toward trustworthy computer aided diagnostic systems for the early detection of oral cancer in clinical settings, the suggested framework shows promise.

Related papers

• A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation [0.0]
  NA methylation profiling has become a powerful approach for central nervous system ( CNS) tumor classification.<n>We propose a novel and methodologically rigorous machine-learning approach for methylation-based CNS tumor classification.<n>Our results show that the proposed model consistently outperforms the previous state of the art across evaluation settings.
  arXiv  Detail & Related papers  (2026-07-01T16:57:58Z)
• DB-FGA-Net: Dual Backbone Frequency Gated Attention Network for Multi-Class Brain Tumor Classification with Grad-CAM Interpretability [0.0]
  We propose a double-backbone network integrating VGG16 and Xception with a Frequency-Gated Attention (FGA) Block to capture complementary local and global features.<n>Our model achieves state-of-the-art performance without augmentation which demonstrates robustness to variably sized and distributed datasets.<n>For further transparency, Grad-CAM is integrated to visualize the tumor regions based on which the model is giving prediction, bridging the gap between model prediction and clinical interpretability.
  arXiv  Detail & Related papers  (2025-10-23T07:39:00Z)
• A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer [54.58205672910646]
  RenalCLIP is a visual-language foundation model for characterization, diagnosis and prognosis of renal mass.<n>It achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer.
  arXiv  Detail & Related papers  (2025-08-22T17:48:19Z)
• Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction [0.0]
  Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates.<n>Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions.<n>This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer.
  arXiv  Detail & Related papers  (2025-06-11T21:26:13Z)
• Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models [40.325359811289445]
  This paper uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data.<n>The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes.
  arXiv  Detail & Related papers  (2025-06-06T11:52:07Z)
• Adaptive Deep Learning for Multiclass Breast Cancer Classification via Misprediction Risk Analysis [0.8028869343053783]
  Early detection is crucial for improving patient outcomes.<n>Computer-aided diagnostic approaches have significantly enhanced breast cancer detection.<n>However, these methods face challenges in multiclass classification, leading to frequent mispredictions.
  arXiv  Detail & Related papers  (2025-03-17T03:25:28Z)
• Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification [7.002657345547741]
  Non-small cell lung cancer (NSCLC) is a predominant cause of cancer mortality worldwide. In this paper, we introduce an innovative integration of multi-modal data, synthesizing fused medical imaging (CT and PET scans) with clinical health records and genomic data. Our research surpasses existing approaches, as evidenced by a substantial enhancement in NSCLC detection and classification precision.
  arXiv  Detail & Related papers  (2024-09-27T12:59:29Z)
• Fairness Evolution in Continual Learning for Medical Imaging [47.52603262576663]
  This study examines how bias evolves across tasks using domain-specific fairness metrics and how different CL strategies impact this evolution.<n>Our results show that Learning without Forgetting and Pseudo-Label achieve optimal classification performance, but Pseudo-Label is less biased.
  arXiv  Detail & Related papers  (2024-04-10T09:48:52Z)
• Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration [54.76511683427566]
  This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information. A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction. The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
  arXiv  Detail & Related papers  (2024-02-16T05:16:20Z)
• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• 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)
• Open-Set Recognition of Breast Cancer Treatments [91.3247063132127]
  Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown" We apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.
  arXiv  Detail & Related papers  (2022-01-09T04:35:55Z)

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.