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Found 8 papers, 1 papers with code
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach
The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment.
Automatic Polyp Segmentation Using Convolutional Neural Networks
The risk of developing colorectal cancer could be reduced by early diagnosis of polyps during a colonoscopy.
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks
Breast cancer is one of the leading causes of death across the world in women.
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification
Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures.
Breast Cancer Diagnosis with Transfer Learning and Global Pooling
Breast cancer is one of the most common causes of cancer-related death in women worldwide.
k-Relevance Vectors: Considering Relevancy Beside Nearness
We show that the new parameter improves the classification accuracy of k-RV.
Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features
In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials.
Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: Bank Branches
To achieve this purpose, this study presents an integrated approach based on Data Envelopment Analysis (DEA), Clustering algorithms and Polynomial Pattern Classifier for constructing a classifier to identify a class of bank branches.
