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CNN-based image analysis for malaria diagnosis
TLDR
This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. Expand
Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images
TLDR
This paper presents an approach that combines conventional image processing with deep learning by fusing the features from the individual techniques. Expand
Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis With Fusion-Based Classification
TLDR
Cervical cancer, which has been affecting women worldwide as the second most common cancer, can be cured if detected early and treated well. Expand
A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification
TLDR
Cervical Cancer, Clinical Decision Support Systems, Convolutional Neural Networks, Data Fusion, Deep Learning, Feature Extraction, Image Classification International Journal of Healthcare Information Systems and Informatics Volume 14 • Issue 2 • April-June 2019 Cervicalcancer. Expand
Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening
TLDR
Cervical cancer is one of the leading causes of women's mortality worldwide. Expand
A demonstration of automated visual evaluation of cervical images taken with a smartphone camera
We examined whether automated visual evaluation (AVE), a deep learning computer application for cervical cancer screening, can be used on cervix images taken by a contemporary smartphone camera. AExpand
Cross-Dataset Evaluation of Deep Learning Networks for Uterine Cervix Segmentation
TLDR
We present 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. Expand
Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening
TLDR
We present a novel ensemble deep learning method to identify cervix images and non-cervix images in a smartphone-acquired cervical image dataset. Expand
Enhancements in localized classification for uterine cervical cancer digital histology image assessment
TLDR
We introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. Expand
Anatomical landmark segmentation in uterine cervix images using deep learning
TLDR
We present a process pipeline which consists of deep learning os region segmentation over multiple datasets, followed by comprehensive evaluation of the performance. Expand
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