Identifikasi Tingkat Manis Buah Belimbing Berdasarkan Citra Red Green Blue Menggunakan Fuzzy Neural Network

  Fuzzy Neural Network (FNN) has a capability to classify a pattern within two different classes which a classical Neural Network (NN) is failed to do so. The fuzzy pattern classification use membership degree on output of neuron as learning target. This research aim is to develop an artificial intelligence system model for non-destructive classification of starfruit using Fuzzy Neural Network. The input parameter is the estimator parameter of starfruit sweet level of red, green and blue index color obtained from image processing. The best result of starfruit sweet level identification using FNN with three classification class target (sour, medium and sweet) is achieved with 25 neurons in hidden layer and 14th epoch with 100% accuracy.   Keyword : classification, fuzzy neural network, starfruit, non-destructive grading, pattern recognition.   &nbsp

Fruit Categorization Technique by using Fuzzy Logic and Neural Network

Before fruits can be issued to the consumers, the fruits will be going through thorough processes and one of the processes is grading. The fruits will be graded according to the standard. The standard is based on the fruits’ country of origin (Malaysian Standard, MS and FAMA Standard). This project is a Matlab simulation of fruits categorization (grading) using artificial intelligent (AI) technique (Fuzzy Logic and Artificial Neural Network) in order to overcome problems faced on the existing system or current method. It is also to ease, fasten the process of fruit grading, and produce consistent and accurate result. Since there are numerous types of fruits, this project will only be focusing on the grading of mangoes, papayas and starfruits or carambola. The input of the system will be the properties that needed to determine the grade of the fruits such as weight, color, shape and the exterior condition of the fruits (defect). Rather than using hardware such as scanner, camera to automatically detect or to give input to the system, the input of the system will be manually keyed in by user. The data of the input will be processed using Matlab Fuzzy logic (FL) and Neural Network (NN) toolbox. The system will process the input with the reference data programmed in the system. The output of the system will be the grade and size of the fruit

Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm

BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.Comment: Accepted at ICIP 201