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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
Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images
TLDR
A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. Expand
Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels
TLDR
In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Expand
Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images.
TLDR
In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal,CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia . Expand
Dense Convolutional Networks With Focal Loss and Image Generation for Electrocardiogram Classification
TLDR
We propose a novel end-to-end learnable architecture based on Dense Convolutional Networks (DCN) for the classification of electrocardiogram (ECG) signals. 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 Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery
TLDR
The multi-label classification problem in Unmanned Aerial Vehicle (UAV) images is particularly challenging compared to single label classification due to its combinatorial nature. 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
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