Cervical Cancer Detection Using Multi-Branch Deep Learning Model

• URL: http://arxiv.org/abs/2408.10498v1
• Date: Tue, 20 Aug 2024 02:44:48 GMT
• Title: Cervical Cancer Detection Using Multi-Branch Deep Learning Model
• Authors: Tatsuhiro Baba, Abu Saleh Musa Miah, Jungpil Shin, Md. Al Mehedi Hasan,
• Abstract summary: 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.
• Score: 0.6249768559720121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cervical cancer is a crucial global health concern for women, and the persistent infection of High-risk HPV mainly triggers this remains a global health challenge, with young women diagnosis rates soaring from 10\% to 40\% over three decades. While Pap smear screening is a prevalent diagnostic method, visual image analysis can be lengthy and often leads to mistakes. Early detection of the disease can contribute significantly to improving patient outcomes. In recent decades, many researchers have employed machine learning techniques that achieved promise in cervical cancer detection processes based on medical images. In recent years, many researchers have employed various deep-learning techniques to achieve high-performance accuracy in detecting cervical cancer but are still facing various challenges. 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). The proposed method leverages the strengths of both MHSA mechanisms and CNN to effectively capture both local and global features within cervical images in two streams. MHSA facilitates the model's ability to focus on relevant regions of interest, while CNN extracts hierarchical features that contribute to accurate classification. Finally, we combined the two stream features and fed them into the classification module to refine the feature and the classification. To evaluate the performance of the proposed approach, we used the SIPaKMeD dataset, which classifies cervical cells into five categories. Our model achieved a remarkable accuracy of 98.522\%. This performance has high recognition accuracy of medical image classification and holds promise for its applicability in other medical image recognition tasks.

Related papers

• CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities [0.05869347864780635]
  We present CerviXpert, a multi-structural Convolutional Neural Network to identify cervix cancer. Cervical cancer affects millions of women worldwide and has a significantly higher survival rate when diagnosed early.
  arXiv  Detail & Related papers  (2024-09-10T05:08:26Z)
• Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion [68.45407109385306]
  In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data. In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
  arXiv  Detail & Related papers  (2023-11-30T15:11:03Z)
• 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)
• A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification [10.61705267657852]
  Cervical cancer is one of the most severe diseases threatening women's health. We propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble. The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cervical cancer.
  arXiv  Detail & Related papers  (2023-08-05T03:21:12Z)
• Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures [3.883460584034766]
  The survival rates for breast cancer patients in certain third-world countries, like South Africa, are alarmingly low. Medical specialists and researchers have turned to domain-specific AI approaches, specifically deep learning models, to develop end-to-end solutions. This research focuses on evaluating the performance of various cutting-edge convolutional neural network (CNN) architectures in comparison to a relatively new model called the Vision Trans-former (ViT)
  arXiv  Detail & Related papers  (2023-05-31T15:21:34Z)
• Recent trends and analysis of Generative Adversarial Networks in Cervical Cancer Imaging [0.0]
  Cervical cancer contributes to 6-29% of all cancers in women. Early detection of this disease helps in better treatment and survival rate of the patient. Generative Adversarial Networks (GANs) are catching up with speed in the screening, detection, and classification of cervical cancer.
  arXiv  Detail & Related papers  (2022-09-23T05:45:40Z)
• 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)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Identification of Cervical Pathology using Adversarial Neural Networks [8.364276127015255]
  Cervical cancer is the leading cause of cancer related deaths in women in India and other low and middle income countries. We propose a convolutional autoencoder based framework, having an architecture similar to SegNet. The proposed method outperforms the standard technique of fine-tuning convolutional neural networks pre-trained on ImageNet database with an average accuracy of 73.75%.
  arXiv  Detail & Related papers  (2020-04-28T10:22:16Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)

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.