scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Pengenalan Jenis Kayu Berdasarkan Citra Makroskopik Menggunakan Metode Convolutional Neural Network

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Eko Yudhi Prastowo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Building Materials ◽  
Raw Materials ◽  
Image Data ◽  
Recognition System ◽  
Fiber Direction ◽  
Processed Products ◽  
The Right

Until now, wood has an irreplaceable function. Building materials, shipping, furniture, sports equipment, carvings and handicrafts using wood. Indonesia has more than 4,000 types of wood, so choosing the right wood is a challenge because choosing the wrong type of wood can make the quality of processed products decline and not as expected. In addition, proper identification of timber can also prevent illegal logging, especially on certain types of wood which are now increasingly scarce. Recognition to wood by looking directly is a difficult thing for ordinary people to do and can only be done by a wood expert, so it is necessary to find a method of recognizing wood that can be used by people independently. One method that can be used to identify type of wood is image processing based on characteristics of wood which include color, fiber direction and texture. This paper will describe recognition of wood-based image processing using Convolutional Neural Network (CNN) method. This method is derived from Neural Networks with addition of an extraction layer feature, which can reduce free parameters that are not needed by the system. Wood image data used in this study are four types of wood that are often used as raw materials for making houses and furniture, namely Glugu, Teak, Sengon and Waru. Results of this study were able to recognize four types of wood with an accuracy of 95% in 600 epochs/iteration, so that it can be used as a simple, easy and inexpensive wood recognition system.

scholarly journals Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Jalil Rozaqi ◽  
Muhammad Rudyanto Arief ◽  
Andi Sunyoto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Method Development ◽  
Classification Model ◽  
Learning Method ◽  
Potato Leaf ◽  
Leaf Disease

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

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