scholarly journals Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN)

2020 ◽  
Vol 22 (2) ◽  
pp. 117-123
Author(s):  
Errissya Rasywir ◽  
Rudolf Sinaga ◽  
Yovi Pratama
Keyword(s):  
Oil Palm ◽  
Data Model ◽  
Recognition Accuracy ◽  
Image Data ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Training Data ◽  
Pests And Diseases ◽  
Average Accuracy

Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.

EFFICIENT CLASSIFICATION OF SCANNED MEDIA USING SPATIAL STATISTICS

2010 ◽  
Vol 24 (06) ◽  
pp. 917-946
Author(s):  
GOZDE UNAL ◽  
GAURAV SHARMA ◽  
REINER ESCHBACH
Keyword(s):  
Spatial Statistics ◽  
High Reliability ◽  
Image Data ◽  
Training Data ◽  
Automated Classification ◽  
Data Set ◽  
Statistical Measures ◽  
Scanned Image ◽  
Scanned Images

Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.

scholarly journals Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

2018 ◽  
Vol 38 (3) ◽  
Author(s):  
Miao Wu ◽  
Chuanbo Yan ◽  
Huiqiang Liu ◽  
Qian Liu
Keyword(s):  
Neural Networks ◽  
Ovarian Cancer ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Training Data ◽  
Serous Carcinoma ◽  
Deep Convolutional Neural Networks ◽  
Ovarian Cancers ◽  
Cancer Types

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

scholarly journals Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

2020 ◽  
Vol 8 (4) ◽  
pp. 304-310
Author(s):  
Windra Swastika ◽  
Ekky Rino Fajar Sakti ◽  
Mochamad Subianto
Keyword(s):  
High Resolution ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Super Resolution ◽  
Training Data ◽  
License Plate ◽  
Plate Number ◽  
Average Accuracy ◽  
Number Recognition ◽  
High Resolution Images

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.

scholarly journals Watermelon Quality Classification Using Weighted K-means Algortihm Based On Feautures YCbCr Color

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Lalu Zulfikar Muslim ◽  
I Gede Pasek Suta Wijaya ◽  
Fitri Bimantoro
Keyword(s):  
Input Data ◽  
Computing Time ◽  
Image Data ◽  
Training Data ◽  
Business Strategies ◽  
Computational Time ◽  
Training Phase ◽  
Quality Classification ◽  
Two Stages

the classification of fruit quality on a computer using image data is very necessary. In addition, this can also be used in making decisions and policies related to business strategies in the industry. In this research, the quality classification of watermelon was carried out using the Weighted K-Means Algorithm. The classification of watermelon fruit in this study was divided into three groups, namely fresh, medium, and rotten. The classification process in the system created is divided into two stages, namely training and examinations.The data that is input into the system is watermelon image data in YCbCr format. In the training phase, the input data that is processed is image data that has been classified. As for the testing/classification phase, the input data processed is an arbitrary image that has not been classified.The results of the classification with watermelon case studies using the weighted k-means algorithm obtained a conclusion that the greater the amount of training data, the computing time needed for the training and testing process will increase, as well as the level of accuracy, precision and recall of the classification results obtained will also get better. While the greater the number of k values, the computational time needed for the training and testing process will increase, but the level of accuracy, precision, and recall of the results of the classification that gets smaller.

scholarly journals CLASSIFICATION UNDER LABEL NOISE BASED ON OUTDATED MAPS

2017 ◽  
Vol IV-1/W1 ◽  
pp. 215-222 ◽  
Author(s):  
A. Maas ◽  
F. Rottensteiner ◽  
C. Heipke
Keyword(s):  
Classical Method ◽  
Image Data ◽  
Training Data ◽  
Label Noise ◽  
Training Technique ◽  
Class Labels ◽  
Noise Tolerant ◽  
Changes Over Time ◽  
Over Time

Supervised classification of remotely sensed images is a classical method for change detection. The task requires training data in the form of image data with known class labels, whose manually generation is time-consuming. If the labels are acquired from the outdated map, the classifier must cope with errors in the training data. These errors, referred to as label noise, typically occur in clusters in object space, because they are caused by land cover changes over time. In this paper we adapt a label noise tolerant training technique for classification, so that the fact that changes affect larger clusters of pixels is considered. We also integrate the existing map into an iterative classification procedure to act as a prior in regions which are likely to contain changes. Our experiments are based on three test areas, using real images with simulated existing databases. Our results show that this method helps to distinguish between real changes over time and false detections caused by misclassification and thus improves the accuracy of the classification results.

scholarly journals Wheat Ear Recognition Based on RetinaNet and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4845
Author(s):  
Jingbo Li ◽  
Changchun Li ◽  
Shuaipeng Fei ◽  
Chunyan Ma ◽  
Weinan Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recognition Accuracy ◽  
Image Data ◽  
Growth Stages ◽  
Data Sets ◽  
Yield Estimation ◽  
Ear Recognition ◽  
Average Accuracy ◽  
Different Growth Stages

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation.

scholarly journals Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yunong Tian ◽  
Guodong Yang ◽  
Zhe Wang ◽  
En Li ◽  
Zize Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Optical Sensors ◽  
Data Augmentation ◽  
Image Data ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Training Data ◽  
Yield Reduction ◽  
Learning Technology

Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

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 Multi-Class Classification of Lung Diseases Using CNN Models

2021 ◽  
Vol 11 (19) ◽  
pp. 9289
Author(s):  
Min Hong ◽  
Beanbonyka Rim ◽  
Hongchang Lee ◽  
Hyeonung Jang ◽  
Joonho Oh ◽  
...  
Keyword(s):  
Lung Diseases ◽  
Image Data ◽  
National Institutes Of Health ◽  
Fine Tuning ◽  
University Hospital ◽  
Improve Performance ◽  
Average Sensitivity ◽  
Average Accuracy ◽  
Multi Class Classification

In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.

scholarly journals Based On K-means Disease Diagnosis Research

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Jiaqi Wu
Keyword(s):  
Medical Science ◽  
Disease Diagnosis ◽  
Modern Medicine ◽  
Disease Classification ◽  
Scientific Management ◽  
Data Set ◽  
Sample Data ◽  
Classification Of Diseases ◽  
Diagnosis Of Diseases

Abstract： For the diagnosis of diseases, modern medicine usually searches for diseases in the disease database to find the type of disease that matches them. The diagnosis of diseases is the first step in treatment. Then the classification of diseases is the basis of disease diagnosis. Disease classification plays an extremely important role in the scientific management of medical records and the development of modern medicine, and is a bridge connecting modern medical science. Therefore, the classification of diseases is very necessary. Based on this, this article establishes a K-means model for disease diagnosis, and combines the internationally unified disease type code ICD statistics table to classify the sample data set into infectious and parasitic diseases, tumors, diabetes and circulatory diseases The training is perfect, and finally the diagnosis classification of the disease is realized.

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