scholarly journals Improving Hierarchical Decision Approach for Single Image Classification of Pap Smear

2018 ◽  
Vol 8 (6) ◽  
pp. 5415
Author(s):  
Dwiza Riana ◽  
Yudi Ramdhani ◽  
Rizki Tri Prasetio ◽  
Achmad Nizar Hidayanto
Keyword(s):  
Genetic Algorithm ◽  
Image Classification ◽  
Pap Smear ◽  
Learning Rate ◽  
Training Data ◽  
Pap Smears ◽  
Single Image ◽  
New Model ◽  
Hierarchical Decision

The single image classification of Pap smears is an important part of the early detection of cervical cancer through Pap smear tests. Unfortunately, most classification processes still require accuracy enhancement, especially to complete the classification in seven classes and to get a qualified classification process. In addition, attempts to improve the single image classification of Pap smears were performed to be able to distinguish normal and abnormal cells. This study proposes a better approach by providing different handling of the initial data preparation process in the form of the distribution for training data and testing data so that it resulted in a new model of Hierarchial Decision Approach (HDA) which has the higher learning rate and momentum values in the proposed new model. This study evaluated 20 different features in hierarchical decision approach model based on Neural Network (NN) and genetic algorithm method for single image classification of Pap smear which resulted in classification experiment using value learning rate of 0.3 and momentum of 0.2 and value of learning rate of 0.5 and momentum of 0.5 by generating classification of 7 classes (Normal Intermediate, Normal Colummar, Mild (Light) Dyplasia, Moderate Dyplasia, Servere Dyplasia and Carcinoma In Situ) better. The accuracy value enhancemenet were also influenced by the application of Genetic Algorithm to feature selection. Thus, from the results of model testing, it can be concluded that the Hierarchical Decision Approach (HDA) method for Pap Smear image classification can be used as a reference for initial screening process to analyze Pap Smear image classification.

Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

2019 ◽  
Vol 34 (03) ◽  
pp. 2051004
Author(s):  
Jian Liu ◽  
Yuchen Zheng ◽  
Ke Dong ◽  
Haitong Yu ◽  
Jianjun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Image Recognition ◽  
Classification Accuracy ◽  
Recommendation System ◽  
Computation Time ◽  
Training Data ◽  
Computational Time ◽  
Time Problem

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.

scholarly journals Image classification of malaria using hybrid algorithms: convolutional neural network and method to find appropriate K for K-nearest neighbor

2019 ◽  
Vol 16 (1) ◽  
pp. 382
Author(s):  
Wisit Lumchanow ◽  
Sakol Udomsiri
Keyword(s):  
Neural Network ◽  
Plasmodium Falciparum ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Plasmodium Vivax ◽  
Nearest Neighbor ◽  
Learning Rate ◽  
K Nearest Neighbor ◽  
Ring Form

<span>This paper presents image classification algorithms to improve the learning rate and to comparison the classification efficiency. Using convolutional neural network (CNN) for feature extraction and method to find appropriate k for k-nearest neighbor (KNN). Medical datasets were used in the experiments to classify <span>Plasmodium Vivax and Plasmodium Falciparum. Results of the study indicated that for Plasmodium Vivax in ring form, the appropriate k was 1 and the learning rate (LR) was 83.33%, Trophozoite (k=5, LR=91.67%),</span></span><span> Schizont (k=1, LR=83.33<span>%</span>), and Gametocyte (k=1, LR=<span lang="AR-SA" dir="RTL">91.67</span><span>%</span>) whereas </span><span>Plasmodium Falciparum in ring form</span><span> (k=7, LR=91.67%)<span>,</span> Trophozoite (k=1, LR=83.33%), Schizont (k=1, LR=91.67%) and Gametocyte (k=1, LR=100%).</span>

Automated image classification of outlet glaciers in Greenland using deep learning

2020 ◽  
Author(s):  
Melanie Marochov ◽  
Patrice Carbonneau ◽  
Chris Stokes
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Supervised Classification ◽  
Greenland Ice Sheet ◽  
Training Data ◽  
Glacier Change ◽  
Outlet Glacier ◽  
Glacial Environments

&lt;p&gt;In recent decades, a wealth of research has focused on elucidating the key controls on the mass loss of the Greenland Ice Sheet and its response to climate forcing, specifically in relation to the drivers of spatio-temporally variable outlet glacier change. Despite the increasing availability of high-resolution satellite data, the time-consuming nature of the manual methods traditionally used to analyse satellite imagery has resulted in a significant bottleneck in the monitoring of outlet glacier change. Recent advances in deep learning applied to image processing have opened up a new frontier in the area of automated delineation of glacier termini. However, at this stage, there remains a paucity of research on the use of deep learning for image classification of outlet glacier landscapes. In this contribution, we apply a deep learning approach based on transfer learning to automatically classify satellite images of Helheim glacier, the fastest flowing outlet glacier in eastern Greenland. The method uses the well-established VGG16 convolutional neural network (CNN), and is trained on 224x224 pixel tiles derived from Sentinel-2 RGB bands, which have a spatial resolution of 10 metres. Based on features learned from ImageNet and limited training data, our deep learning model can classify glacial environments with &gt;85% accuracy. In future stages of this research, we will use a new method originally developed for fluvial settings, dubbed &amp;#8216;CNN-Supervised Classification&amp;#8217; (CSC). CSC uses a pre-trained CNN (in this case our VGG16 model) to replace the human operator&amp;#8217;s role in traditional supervised classification by automatically producing new label data to train a pixel-level neural network classifier for any new image. This transferable approach to image classification of outlet glacier landscapes permits not only automated terminus delineation, but also facilitates the efficient analysis of numerous processes controlling outlet glacier behaviour, such as fjord geometry, subglacial plumes, and supra-glacial lakes.&lt;/p&gt;

scholarly journals Klasifikasi Citra Histopatologi Kanker Payudara menggunakan Data Resampling Random dan Residual Network

2021 ◽  
Vol 11 (1) ◽  
pp. 70-77
Author(s):  
Wahyudi Setiawan
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Training Data ◽  
Unbalanced Data ◽  
Residual Network ◽  
Average Precision ◽  
Average Recall ◽  
Average Accuracy ◽  
Optimal Classification

Data imbalance between classes is one of the problems on image classification. The data in each class is not equal and has a relatively large difference generated in less than optimal classification results. Ideally, the data in each class is equal or have a slight difference. This article discusses the classification of the histopathology breast cancer image. The data consist of  8 classes with unbalanced data. The method for balancing the data in each class uses random resampling which is applied to training data only. The data used from BreakHist with magnifications of 40x, 100x, 200x, and 400x . The classification uses Residual Network (ResNet) 18 and 50. The best results are obtained on images with a magnification of 400x. Classification results using ResNet18 has an average accuracy of 79.82%, an average precision of 71.39%, and an average recall of 82.37%. Meanwhile using ResNet50 showed an average accuracy of 81.67%, average precision of 78.41%, and an average recall of 82.91%.

scholarly journals Segmentation and analysis of Pap smear microscopic images using the K-means and J48 algorithms

2021 ◽  
Vol 9 (2) ◽  
pp. 113-119
Author(s):  
Sri Hadianti ◽  
Dwiza Riana
Keyword(s):  
Cervical Cancer ◽  
Early Detection ◽  
Inflammatory Cell ◽  
Pap Smear ◽  
Inflammatory Cells ◽  
Training Data ◽  
Feature Analysis ◽  
Analysis Method ◽  
Microscopic Images

A Pap smear is used to early detection cervical cancer. This study proposes the segmentation and analysis method of Pap smear cells images using the K-means algorithm so that cytoplasmic cells, nuclear cells, and inflammatory cells can be segmented automatically. The results of the feature analysis from the cytoplasmic, nuclear, and inflammatory cell images were classified using the J48 algorithm with 37 training data. The training resulted in an accuracy of 94.594 %, precision of 95 %, and sensitivity of 94.6 %. The classification of 24 testing images resulted in an accuracy of 91.6%, a precision of 92.5 %, and a sensitivity of 91.7 %.

scholarly journals Classification of Similar Sports Images Using Convolutional Neural Network with Hyper-Parameter Optimization

2020 ◽  
Vol 10 (23) ◽  
pp. 8494
Author(s):  
Vili Podgorelec ◽  
Špela Pečnik ◽  
Grega Vrbančič
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Parameter Optimization ◽  
Classification Performance ◽  
Training Data ◽  
Fine Tuning ◽  
American Football ◽  
Massive Scale

With the exponential growth of the presence of sport in the media, the need for effective classification of sports images has become crucial. The traditional approaches require carefully hand-crafted features, which make them impractical for massive-scale data and less accurate in distinguishing images that are very similar in appearance. As the deep learning methods can automatically extract deep representation of training data and have achieved impressive performance in image classification, our goal was to apply them to automatic classification of very similar sports disciplines. For this purpose, we developed a CNN-TL-DE method for image classification using the fine-tuning of transfer learning for training a convolutional neural network model with the use of hyper-parameter optimization based on differential evolution. Through the automatic optimization of neural network topology and essential training parameters, we significantly improved the classification performance evaluated on a dataset composed from images of four similar sports—American football, rugby, soccer, and field hockey. The analysis of interpretable representation of the trained model additionally revealed interesting insights into how our model perceives images which contributed to a greater confidence in the model prediction. The performed experiments showed our proposed method to be a very competitive image classification method for distinguishing very similar sports and sport situations.

scholarly journals Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification

2018 ◽  
Vol 1 (2) ◽  
pp. 60 ◽  
Author(s):  
Jasril Jasril ◽  
Suwanto Sanjaya
Keyword(s):  
Image Segmentation ◽  
Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Muslim Country ◽  
Test Results ◽  
Fuzzy C Means ◽  
Testing Data ◽  
Total Data

Base on some cases in Indonesia, meat sellers often mix beef and pork. Indonesia is a predominantly Muslim country. Pork is forbidden in Islam. In this research, the classification of beef and pork image was performed. Spatial Fuzzy C-Means is used for image segmentation. GLCM and HSV are used as a feature of segmentation results. LVQ3 is used as a method of classification. LVQ3 parameters tested were the variety of learning rate values and window values. The learning rate values used is 0.0001; 0.01; 0.1; 0.4; 0.7; 0.9 and the window values used is 0.0001; 0.4; 0.7. The training data used is 90% of the total data, and the testing data used is 10%. Maximum epoch used is 1000 iterations. Based on the test results, the highest accuracy was 91.67%.

scholarly journals Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models

2021 ◽  
Vol 27 (4) ◽  
pp. 298-306
Author(s):  
Audrey K. C. Huong ◽  
Kim Gaik Tay ◽  
Xavier T. I. Ngu
Keyword(s):  
Image Classification ◽  
Pap Smear ◽  
False Negative ◽  
Area Under The Curve ◽  
Superior Performance ◽  
Support Vector ◽  
Training Time ◽  
Binary Coding ◽  
Svm Model

Objectives: Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem.Methods: This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction.Results: Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%.Conclusions: We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.

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