Comparative Analysis of Machine Learning and Deep Learning Models for Classifying Squamous Epithelial Cells of the Cervix

• URL: http://arxiv.org/abs/2411.13535v1
• Date: Wed, 20 Nov 2024 18:37:01 GMT
• Title: Comparative Analysis of Machine Learning and Deep Learning Models for Classifying Squamous Epithelial Cells of the Cervix
• Authors: Subhasish Das, Satish K Panda, Madhusmita Sethy, Prajna Paramita Giri, Ashwini K Nanda,
• Abstract summary: Current Pap smear analysis method is manual, time-consuming, labor-intensive, and prone to human error. In this study, we aimed to classify cells in Pap smear images into five categories: superficial-intermediate, parabasal, koilocytes, dyskeratotic, and metaplastic. Various machine learning algorithms, including Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor, were employed for this classification task.
• Score: 0.7119794757408745
• License:
• Abstract: The cervix is the narrow end of the uterus that connects to the vagina in the female reproductive system. Abnormal cell growth in the squamous epithelial lining of the cervix leads to cervical cancer in females. A Pap smear is a diagnostic procedure used to detect cervical cancer by gently collecting cells from the surface of the cervix with a small brush and analyzing their changes under a microscope. For population-based cervical cancer screening, visual inspection with acetic acid is a cost-effective method with high sensitivity. However, Pap smears are also suitable for mass screening due to their higher specificity. The current Pap smear analysis method is manual, time-consuming, labor-intensive, and prone to human error. Therefore, an artificial intelligence (AI)-based approach for automatic cell classification is needed. In this study, we aimed to classify cells in Pap smear images into five categories: superficial-intermediate, parabasal, koilocytes, dyskeratotic, and metaplastic. Various machine learning (ML) algorithms, including Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor, as well as deep learning (DL) approaches like ResNet-50, were employed for this classification task. The ML models demonstrated high classification accuracy; however, ResNet-50 outperformed the others, achieving a classification accuracy of 93.06%. This study highlights the efficiency of DL models for cell-level classification and their potential to aid in the early diagnosis of cervical cancer from Pap smear images.

Related papers

• Cervical Cancer Detection Using Multi-Branch Deep Learning Model [0.6249768559720121]
  This research proposes an innovative and novel approach to automate cervical cancer image classification using Multi-Head Self-Attention (MHSA) and convolutional neural networks (CNNs) Our model achieved a remarkable accuracy of 98.522%, which holds promise for its applicability in other medical image recognition tasks.
  arXiv  Detail & Related papers  (2024-08-20T02:44:48Z)
• Interpretable pap smear cell representation for cervical cancer screening [3.8656297418166305]
  We introduce a method to learn explainable deep cervical cell representations for pap smear images based on one class classification using variational autoencoders. Our model can discriminate abnormality without the need of additional training of deep models.
  arXiv  Detail & Related papers  (2023-11-17T01:29:16Z)
• A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes [44.077241051884926]
  We train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups. We find a significant effect of sex and age bias on the performance of the model for AML subtype classification.
  arXiv  Detail & Related papers  (2023-08-24T09:32:46Z)
• CerviFormer: A Pap-smear based cervical cancer classification method using cross attention and latent transformer [0.1529342790344802]
  This study proposes a cross-attention-based Transfomer approach for the reliable classification of cervical cancer in Pap smear images. The model uses a cross-attention technique to repeatedly consolidate the input data into a compact latent Transformer module. The proposed method brings forth a comprehensive classification model to detect cervical cancer in Pap smear images.
  arXiv  Detail & Related papers  (2023-03-17T19:34:54Z)
• A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron [34.179030555958654]
  This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately.
  arXiv  Detail & Related papers  (2022-06-02T10:16:07Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques [14.208643185430219]
  Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated to classify cervical pap cells. This study proposes a hybrid deep feature fusion (HDFF) technique based on DL to classify the cervical cells accurately.
  arXiv  Detail & Related papers  (2021-02-24T10:34:51Z)

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.