An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture

• URL: http://arxiv.org/abs/2508.11532v1
• Date: Fri, 15 Aug 2025 15:20:25 GMT
• Title: An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
• Abstract summary: This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture.<n>The proposed method enhances feature extraction capability and classification performance while reducing computational complexity.<n> Experimental results demonstrate that the proposed method effectively improves medical image classification in resource-limited settings.
• Score: 1.4587927509092535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while reducing computational complexity. Specifically, the method introduces a dual global pooling (Global Average Pooling and Global Max Pooling) feature fusion strategy into the ConvNeXt-Tiny backbone to simultaneously preserve global statistical features and salient response information. A lightweight channel attention module, termed Squeeze-and-Excitation Vector (SEVector), is designed to improve the adaptive allocation of channel weights while minimizing parameter overhead. Additionally, a Feature Smoothing Loss is incorporated into the loss function to enhance intra-class feature consistency and suppress intra-class variance. Under CPU-only conditions (8 threads), the method achieves a maximum classification accuracy of 89.10% on the test set within 10 training epochs, exhibiting a stable convergence trend in loss values. Experimental results demonstrate that the proposed method effectively improves medical image classification performance in resource-limited settings, providing a feasible and efficient solution for the deployment and promotion of medical imaging analysis models.

Related papers

• ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans [0.0]
  This study introduces a hybrid framework that utilizes the Oriented FAST and Rotated BRIEF (ORB) algorithm for precise feature extraction and a Support Vector Machine (SVM) for classification.<n>The proposed approach achieves a sub- stantial data reduction of approximately 99.5%, which effectively minimizes the influence of non informative background data.<n> Experimental evaluations conducted on the Br35H dataset demonstrate that the framework attains a classification accuracy of 97.5%.
  arXiv  Detail & Related papers  (2026-09-02T09:09:14Z)
• Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images [3.5122049834653706]
  Whole-slide images (WSIs) provide rich tissue-level and cellular-level information.<n>Low-magnification pathology images with limited annotations can achieve performance comparable to high-magnification images.
  arXiv  Detail & Related papers  (2026-07-12T14:23:50Z)
• PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification [1.2744523252873352]
  We propose an explainable hierarchical multi-view ensemble framework for the robust classification of 14 thoracic pathologies.<n>The framework employs view-specific training by independently modeling frontal and lateral radiographs.<n>The framework achieves state-of-the-art macro-average AUROC scores of 0.9319 for frontal and 0.9154 for lateral radiographs.
  arXiv  Detail & Related papers  (2026-07-05T19:50:53Z)
• Intracoronary Optical Coherence Tomography Image Processing and Vessel Classification Using Machine Learning [0.0]
  Intracoronary Optical Coherence Tomography enables high-resolution visualization of coronary vessel anatomy.<n>The proposed method integrates image preprocessing, guidewire artifact removal, polar-to-Cartesian transformation, unsupervised K-means clustering, and local feature extraction.<n> Experimental results demonstrate excellent performance, achieving precision, recall, and F1-score values up to 1.00 and overall classification accuracy of 99.68%.
  arXiv  Detail & Related papers  (2026-02-17T13:47:27Z)
• Implicit Neural Representation-Based Continuous Single Image Super Resolution: An Empirical Study [50.15623093332659]
  Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR)<n>We compare existing techniques across diverse settings and present aggregated performance results on multiple image quality metrics.<n>We examine a new loss function that penalizes intensity variations while preserving edges, textures, and finer details during training.
  arXiv  Detail & Related papers  (2026-01-25T07:09:20Z)
• Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications [0.0]
  The study introduces an intricate deep learning system using Conal Neural Networks for automated pneumonia detection from chest Xray images.<n>The proposed CNN architecture integrates sophisticated methods including separable convolutions along with batch normalization and dropout regularization.<n>A convoluted array of evaluation metrics such as accuracy, precision, recall, and F1 score collectively verify the model exceptional performance by recording an accuracy rate of 91.
  arXiv  Detail & Related papers  (2025-09-26T11:58:50Z)
• ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data [51.095474325541794]
  We introduce ClipGS, an innovative Gaussian splatting framework with the clipping plane supported, for interactive cinematic visualization of medical data.<n>We validate our method on five volumetric medical data, and reach an average 36.635 PSNR rendering quality with 156 FPS and 16.1 MB model size.
  arXiv  Detail & Related papers  (2025-07-09T08:24:28Z)
• 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)
• An incremental algorithm for non-convex AI-enhanced medical image processing [0.0]
  We propose a hybrid framework that integrates model-based optimization and deep learning-based methods to solve inverse problems in medical imaging.<n>We show that incDG outperforms both conventional iterative solvers and deep learning-based methods, achieving superior accuracy and stability.<n>We conclude that incDG does not significantly degrade performance, making it a practical and powerful tool for solving non deblurring problems in imaging beyond.
  arXiv  Detail & Related papers  (2025-05-13T08:03:14Z)
• Decentralized LoRA Augmented Transformer with Context-aware Multi-scale Feature Learning for Secured Eye Diagnosis [2.1358421658740214]
  This paper proposes a novel Data efficient Image Transformer (DeiT) based framework that integrates context aware multiscale patch embedding, Low-Rank Adaptation (LoRA), knowledge distillation, and federated learning to address these challenges in a unified manner.<n>The proposed model effectively captures both local and global retinal features by leveraging multi scale patch representations with local and global attention mechanisms.
  arXiv  Detail & Related papers  (2025-05-11T13:51:56Z)
• Revisiting Medical Image Retrieval via Knowledge Consolidation [46.6989555659494]
  We propose a novel method to consolidate knowledge of hierarchical features and functions.<n>We introduce Depth-aware Representation Fusion (DaRF) and Structure-aware Contrastive Hashing (SCH)<n>Our method achieves a 5.6-38.9% improvement in mean Average Precision on the anatomical radiology dataset.
  arXiv  Detail & Related papers  (2025-03-12T13:16:42Z)
• A Cascaded Dilated Convolution Approach for Mpox Lesion Classification [0.0]
  Mpox virus presents significant diagnostic challenges due to its visual similarity to other skin lesion diseases.<n>Deep learning-based approaches for skin lesion classification offer a promising alternative.<n>This study introduces the Cascaded Atrous Group Attention framework to address these challenges.
  arXiv  Detail & Related papers  (2024-12-13T12:47:30Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration [59.6021678234829]
  We propose a novel method to restore the intermediate features for two sparsely sampled and adjacent video frames. With the integration of our method, the efficiency of three commonly used baselines has been improved by over 50%, with a mere 0.5% reduction in recognition accuracy.
  arXiv  Detail & Related papers  (2023-07-27T13:52:42Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Brain tumor grade classification Using LSTM Neural Networks with Domain Pre-Transforms [0.0]
  We propose a weakly supervised imageclassification method based on combination of hand-craftedfeatures. In this study, we haveexperimented classification of brain tumor grades and achieved the state of the art performance with the resolution of 256 x 256.
  arXiv  Detail & Related papers  (2021-06-21T07:04:52Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.