Convolutional Neural Networks for Automatic Classification of Diseased Leaves: The Impact of Dataset Size and Fine-Tuning

2020 ◽  
pp. 951-966
Author(s):  
Giovanny Caluña ◽  
Lorena Guachi-Guachi ◽  
Ramiro Brito
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Classification ◽  
Fine Tuning ◽  
Dataset Size ◽  
The Impact

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

scholarly journals Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks

2021 ◽  
Vol 9 (7) ◽  
pp. 550-550
Author(s):  
Jiewei Jiang ◽  
Liming Wang ◽  
Haoran Fu ◽  
Erping Long ◽  
Yibin Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Classification

Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

2017 ◽  
Author(s):  
Oscar Jiménez del Toro ◽  
Manfredo Atzori ◽  
Sebastian Otálora ◽  
Mats Andersson ◽  
Kristian Eurén ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gleason Score ◽  
Automatic Classification ◽  
Prostate Tissue ◽  
High Grade

scholarly journals Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

2018 ◽  
Author(s):  
Mikhail Krinitskiy ◽  
Polina Verezemskaya ◽  
Kirill Grashchenkov ◽  
Natalia Tilinina ◽  
Sergey Gulev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Satellite Imagery ◽  
Binary Classification ◽  
Fine Tuning ◽  
Reference Database ◽  
Accuracy Score ◽  
Deep Convolutional Neural Networks ◽  
Deep Ocean Water

Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed manually. Existing algorithms for the automatic detection of polar mesocyclones are based on the conventional analysis of patterns of cloudiness and involve different empirically defined thresholds of geophysical variables. As a result, various detection methods typically reveal very different results when applied to a single dataset. We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). As a first step, we demonstrate that DCNN model is capable of performing binary classification of 500x500km patches of satellite images regarding MC patterns presence in it. The training dataset is based on the reference database of MCs manually tracked in the Southern Hemisphere from satellite mosaics. We use a subset of this database with MC diameters falling in the range of 200-400 km. This dataset is further used for testing several different DCNN setups, specifically, DCNN built “from scratch”, DCNN based on VGG16 pre-trained weights also engaging the Transfer Learning technique, and DCNN based on VGG16 with Fine Tuning technique. Each of these networks is further applied to both infrared (IR) and a combination of infrared and water vapor (IR+WV) satellite imagery. The best skills (97% in terms of the binary classification accuracy score) is achieved with the model that averages the estimates of the ensemble of different DCNNs. The algorithm can be further extended to the automatic identification and tracking numerical scheme and applied to other atmospheric phenomena characterized by a distinct signature in satellite imagery.

Sign in / Sign up

Export Citation Format

Share Document