Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

• URL: http://arxiv.org/abs/2412.20235v1
• Date: Sat, 28 Dec 2024 18:15:07 GMT
• Title: Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
• Authors: Md. Zehan Alam, Tonmoy Roy, H. M. Nahid Kawsar, Iffat Rimi,
• Abstract summary: This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. We integrate the Synthetic Minority Oversampling computation Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%.
• Score: 0.0
• License:
• Abstract: This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL-using pre-trained models on the ImageNet dataset-varies due to domain-specific challenges. We evaluate five pre-trained models-MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201-on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with resampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers.

Related papers

• Efficient Brain Tumor Classification with Lightweight CNN Architecture: A Novel Approach [0.0]
  Brain tumor classification using MRI images is critical in medical diagnostics, where early and accurate detection significantly impacts patient outcomes. Recent advancements in deep learning (DL) have shown promise, but many models struggle with balancing accuracy and computational efficiency. We propose a novel model architecture integrating separable convolutions and squeeze and excitation (SE) blocks, designed to enhance feature extraction while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-02-01T21:06:42Z)
• 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)
• Self-supervised Brain Lesion Generation for Effective Data Augmentation of Medical Images [0.9626666671366836]
  We propose a framework to efficiently generate new samples for training a brain lesion segmentation model. We first train a lesion generator, based on an adversarial autoencoder, in a self-supervised manner. Next, we utilize a novel image composition algorithm, Soft Poisson Blending, to seamlessly combine synthetic lesions and brain images.
  arXiv  Detail & Related papers  (2024-06-21T01:53:12Z)
• Learning-based Bone Quality Classification Method for Spinal Metastasis [36.59899006688448]
  Early detection of spinal metastasis is critical for accurate staging and optimal treatment. In this paper, we explore a learning-based automatic bone quality classification method for spinal metastasis based on CT images.
  arXiv  Detail & Related papers  (2024-02-14T02:53:51Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection [2.359557447960552]
  Vision transformers (ViT) have emerged in recent years as an alternative to CNNs for several computer vision applications. We tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic and real MRI scans.
  arXiv  Detail & Related papers  (2023-03-14T20:18:12Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Classification in Imbalanced Data Sets [0.6802401545890961]
  Machine learning techniques for accurate classification of skin cancer from medical images have been reported. Many techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based on limited amounts of training data. We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with patient information (meta-data) are combined using a meta-learner.
  arXiv  Detail & Related papers  (2021-03-22T06:04:45Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• Predictive Analysis of Diabetic Retinopathy with Transfer Learning [0.0]
  This paper studies the performance of CNN architectures for Diabetic Retinopathy Classification with the help of Transfer Learning. The results indicate that Transfer Learning with ImageNet weights using VGG 16 model demonstrates the best classification performance with the best Accuracy of 95%.
  arXiv  Detail & Related papers  (2020-11-08T18:54:57Z)
• 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)

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.