469 research outputs found
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
The poor contrast and the overlapping of cervical cell cytoplasm are the
major issues in the accurate segmentation of cervical cell cytoplasm. This
paper presents an automated unsupervised cytoplasm segmentation approach which
can effectively find the cytoplasm boundaries in overlapping cells. The
proposed approach first segments the cell clumps from the cervical smear image
and detects the nuclei in each cell clump. A modified Otsu method with prior
class probability is proposed for accurate segmentation of nuclei from the cell
clumps. Using distance regularized level set evolution, the contour around each
nucleus is evolved until it reaches the cytoplasm boundaries. Promising results
were obtained by experimenting on ISBI 2015 challenge dataset.Comment: 4 pages, 4 figures, Biomedical Engineering and Sciences (IECBES),
2016 IEEE EMBS Conference on. IEEE, 201
Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images
Cervical cancer is a prevalent disease affecting millions of women worldwide
every year. It requires significant attention, as early detection during the
precancerous stage provides an opportunity for a cure. The screening and
diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep
learning, a promising technology in computer vision, has emerged as a potential
solution to improve the accuracy and efficiency of cervical cancer screening
compared to traditional clinical inspection methods that are prone to human
error. This review article discusses cervical cancer and its screening
processes, followed by the Deep Learning training process and the
classification, segmentation, and detection tasks for cervical cancer
diagnosis. Additionally, we explored the most common public datasets used in
both cytology and colposcopy and highlighted the popular and most utilized
architectures that researchers have applied to both cytology and colposcopy. We
reviewed 24 selected practical papers in this study and summarized them. This
article highlights the remarkable efficiency in enhancing the precision and
speed of cervical cancer analysis by Deep Learning, bringing us closer to early
diagnosis and saving lives
Combining machine learning and deep learning approaches to detect cervical cancer in cytology images
This dissertation is centred around the implementation and optimization of a hybrid pipeline for the identification and stratification of abnormal cell regions in cytology images, combining state of the art deep learning (DL) approaches and conventional machine learning (ML) models.Cervical cancer is the fourth most common cancer in women. When diagnosed early on, it is one of the most successfully treatable types of cancer. As such, screening tests are very effective as a prevention measure. These tests involve the analysis of microscopic fields of cytology samples which, when performed manually, is a very demanding task, requiring highly specialized laboratory technologists (cytotechs). Due to this, there has been a great interest in automating the overall screening process. Most of these computer-aided diagnosis systems subject the images from each sample to a set of steps, more notably focus and adequacy assessment, region of interest identification and respective classification. This work is focused on the last two stages, more specifically, the detection of abnormal regions and the classification of their abnormality level. The main approaches can be divided into two types: deep learning architectures and conventional machine learning models, both presenting their own set of advantages and disadvantages.
This work explores the combination of both of these approaches in hybrid pipelines to minimize the problems of each one whilst taking advantage of the best they have to offer, ultimately contributing to a decision support system for cervical cancer diagnosis. More specifically, it is proposed a deep-learning approach for the detection of the regions of interest and respective bounding-box generation, followed by a simpler machine-learning model for their classification. Furthermore, a comparative analysis of different hybrid pipelines and algorithms will also be performed, aiming to support future research of similar solutions
Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network
The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms
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