Proceedings ArticleDOI
Cervical Cancer Diagnosis using CervixNet - A Deep Learning Approach
Rohan Gorantla,Rajeev Kumar Singh,Rohan Pandey,Mayank Jain +3 more
- pp 397-404
Reads0
Chats0
TLDR
A novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment and the results obtained validate the approach to provide first level screening at a low cost.Abstract:
Cervical cancer affects 570,000 women globally and is among the most common causes of cancer-related deaths. Cervical cancer is caused due to the Human Papilloma Virus (HPV) which leads to abnormal growth of cells in the cervix region. Regular testing for HPV in women has helped reduce the death rate in developed countries. However, developing nations are still struggling to provide low-cost solutions due to the lack of affordable medical facilities. The skewed ratio of the oncologists to patients has also aggravated the problem. Motivated by the Deep Learning solutions in Bio-medical imaging, we propose a novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment. For the classification task, a novel Hierarchical Convolutional Mixture of Experts (HCME) algorithm is proposed. HCME is capable of tackling the problem of overfitting, given that small datasets are an inherent problem in the field of biomedical imaging. Our proposed methodology has outperformed all the existing methodologies on publicly available Intel and Mobile-ODT Kaggle dataset giving an Accuracy of 96.77% and kappa score of 0.951. Hence, the results obtained validate our approach to provide first level screening at a low cost.read more
Citations
More filters
Journal ArticleDOI
COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.
TL;DR: A novel Deep Learning based solution to rapidly classify COVID -19 patient using chest X-Ray using a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner and an effective pruning strategy results in increased model performance, generalisability, and decreased model complexity.
Journal ArticleDOI
Deep Learning in Selected Cancers' Image Analysis-A Survey.
Taye Girma Debelee,Taye Girma Debelee,Samuel Rahimeto Kebede,Friedhelm Schwenker,Zemene Matewos Shewarega +4 more
TL;DR: The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification.
Journal ArticleDOI
Cervical Cancer Diagnosis Using Very Deep Networks Over Different Activation Functions
TL;DR: In this article, three residual networks of the same structure are built with different activation functions, and the experimental results showed that designed residual networks with leaky and parametric rectified linear unit (Leaky-RELU and PRELU) activation functions performed almost equally in terms of accuracy where they reached accuracies of 90.2 and 100%, respectively.
Journal ArticleDOI
Detection of cervical cells based on improved SSD network
TL;DR: The intent of this study is to classify the cervical cells through deep learning models, which helps to monitor the patients’ health and concludes that the proposed SSD network could be applied in cell classification for the early automatic detection of cervical cancer.
Journal ArticleDOI
Cross-Dataset Evaluation of Deep Learning Networks for Uterine Cervix Segmentation
TL;DR: An evaluation of two state-of-the-art deep learning-based object localization and segmentation methods, viz., Mask R-convolutional neural network (CNN) and MaskX R-CNN, for automatic cervix segmentation using three datasets.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Related Papers (5)
Cervical Cancer Risk Classification Based on Deep Convolutional Neural Network
Durrabida Zahras,Zuherman Rustam +1 more
Classification of imbalanced oral cancer image data from high-risk population
Bofan Song,Shaobai Li,Sumsum P. Sunny,Keerthi Gurushanth,Pramila Mendonca,Nirza Mukhia,Sanjana Patrick,Shubha Gurudath,Subhashini Raghavan,Imchen Tsusennaro,Shirley T Leivon,Trupti Kolur,Vivek Shetty,Vidya Bushan,Rohan Ramesh,Tyler Peterson,Vijay Pillai,Petra Wilder-Smith,Alben Sigamani,Amritha Suresh,Moni Abraham Kuriakose,Praveen Birur,Rongguang Liang +22 more