Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset

• URL: http://arxiv.org/abs/2309.10829v2
• Date: Tue, 3 Oct 2023 21:45:52 GMT
• Title: Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset
• Authors: Shabbir Ahmed Shuvo, Md Aminul Islam, Md. Mozammel Hoque, Rejwan Bin Sulaiman
• Abstract summary: We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18. We found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision. Keywords: Pulmonary, Deep Learning, Tuberculosis, Disease detection, Xray

Related papers

• Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification [0.0]
  This study introduces a novel and accurate approach to breast cancer classification using histopathology images. It systematically compares leading Convolutional Neural Network (CNN) models across varying image datasets. Our findings establish the settings required to achieve exceptional classification accuracy for standalone CNN models.
  arXiv  Detail & Related papers  (2024-10-04T11:31:43Z)
• 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)
• SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis [3.1542695050861544]
  Deep learning-based supervised methods are widely popular in medical image analysis. They require a large amount of training data and face issues in generalisability to unseen datasets. We propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin.
  arXiv  Detail & Related papers  (2023-05-31T21:28:08Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs [43.43732218093039]
  We compare a popular thoracic disease classification model, CheXNet, and a thoracic lesion detection model, CheXDet. We found incorporating external training data even led to performance degradation for CheXNet. By visualizing the models' decision-making regions, we revealed that CheXNet learned patterns other than the target lesions.
  arXiv  Detail & Related papers  (2021-04-21T14:21:37Z)
• 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)
• Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures [83.48996461770017]
  This work presents a Deep Learning method based on the late fusion of different convolutional architectures. We built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool.
  arXiv  Detail & Related papers  (2020-12-23T14:38:35Z)
• 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)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)
• Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset [4.664495510551646]
  This paper presents a pneumonia chest x-ray detection based on generative adversarial networks (GAN) with a fine-tuned deep transfer learning for a limited dataset. The dataset used in this research consists of 5863 X-ray images with two categories: Normal and Pneumonia.
  arXiv  Detail & Related papers  (2020-04-02T08:14:37Z)
• An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs [9.462808515258464]
  Deep learning models often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform significantly worse when tested in different hospital systems. We propose an approach based on adversarial optimization, which allows us to learn more robust models that do not depend on confounders.
  arXiv  Detail & Related papers  (2020-01-13T03:49:05Z)

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.