Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network

Identification of beef and pork using gray level co-occurrence matrix and probabilistic neural network

Objective: Identify images of beef and pork using texture feature extraction Gray Level Co-Occurrence Matrix and Probabilistic Neural Network classification algorithm.Design/method/approach: Apply texture feature extraction to Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier to perform classification.Results: From the test results with k-fold cross-validation and confusion matrix, it shows that feature extraction of Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier get an average accuracy of 87%, precision 83%, and recall 90%.Authenticity/state of the art: In this study, several scenarios were tested, namely the effect of using resize, brightness, and rotate values. Using a resize value of 256 x 256 pixels from the test results got the best accuracy of 87%. The brightness test of 20% affects the accuracy rate of 86% on increasing brightness and 90% on reducing brightness. In contrast, the test on the rotated image does not affect the accuracy results. The average accuracy obtained is 87%. The data in this study were obtained by collecting primary data on images of beef and pork in several markets in Denpasar

Penggunaan Model Multinomial Untuk Mendukung Keputusan Neural Buatan Dalam Klasifikasi dan Deteksi Perubahan Penutup Lahan Citra Multi Waktu dan Multi Sensor

This paper presents the result of continuing study on image classification. In previous study, have recommended a neuro-satistical scheme in the framework of multitemporal optical sensor image classification. Thr scheme consists of neural network classifier to compute the posterior probabilities, expectation maximum method to optimize prior join probabilities, compound probabilities to produce thematic image and change image. This paper report the result of extending the scheme for multidate multisensor image classification. For each sensor image classifier, two scheme have been evaluated. The first scheme has use the co-occurrence matrix  feature images or original tonal images as the input data and the gaussian kernel for the neural network classifier. The second scheme has use the original tonal image as the input data and the multinomial co-occurrence matrix kernel for the neural network classifier. The result are also compared to use of bachh propagation neural network classifier. Base on this study we have proposed a scheme for multidate multisensor image classifier.tThis paper presents the results of continuing study on image classification.  In previous study,  have recommended  a neuro-statistical scheme in the framework of multitemporal optical-sensor image classification.  The scheme consists of  neural network  classifier to compute the posterior probabilities, expectation maximum  method to optimize prior joint probabilities, and compound probabilities to produce thematic image and change image.  This paper reports the results of extending the scheme for multidate multisensor image classification. For each sensor image classifier, two schemes have been evaluated.  The first scheme has used the co-occurrence matrix texture feature images or original tonal images as the input data and the Gaussian kernel for the neural network classifier. The second scheme has used the original tonal image as the input data and the multinomial co-occurrence matrix kernel for the neural network classifier.  Based on this study we have proposed  a scheme for multidate-multisensor image classification.  Key words : Probabilistic Neural Network, Gaussian Model, Multinomial Model, Multisensor Multitemporal.  Makalah ini menyajikan hasil studi berkelanjutan pada klasifikasi citra. Dalam studi sebelumnya, telah direkomendasikan skema neuro-statistik dalam skema multi temporal citra sensor optik. Skema ini terdiri atas pengklasifikasi jaringan neural untuk menghitung probabilitas posterior, metode ekspektasi maksimum untuk mengoptimalkan probabilitas join, dan probabilitas majemuk untuk menghasilkan citra tematik dan citra perubahan penutup lahan. Makalah ini melaporkan hasil perluasan dari skema yang telah ada untuk klasifikasi citra multi waktu – multi sensor.  Untuk setiap pengklasifikasi citra sensor, dua skema telah dilakukan pengujian. Skema pertama menggunakan  fitur tekstur citra tonal asli sebagai data input  Model Gaussian untuk pengklasifikasi jaringan neural. Skema kedua menggunakan fitur tekstur citra asli dan matrik ko-okuren dengan Model Multinomial sebagai data input pengklasifikasi jaringan neural. Berdasarkan studi ini kami merekomendasi sebuah skema untuk klasifikasi citra multi waktu- multi sensor.  Kata Kunci : Probabilistic Neural Network, Model Gaussian, Model Multinomial, Multisensor Multitempora

A Deep Embedding Model for Co-occurrence Learning

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items. In this paper, we study co-occurrence data using a general energy-based probabilistic model, and we analyze three different categories of energy-based model, namely, the $L_1$, $L_2$ and $L_k$ models, which are able to capture different levels of dependency in the co-occurrence data. We also discuss how several typical existing models are related to these three types of energy models, including the Fully Visible Boltzmann Machine (FVBM) ($L_2$), Matrix Factorization ($L_2$), Log-BiLinear (LBL) models ($L_2$), and the Restricted Boltzmann Machine (RBM) model ($L_k$). Then, we propose a Deep Embedding Model (DEM) (an $L_k$ model) from the energy model in a \emph{principled} manner. Furthermore, motivated by the observation that the partition function in the energy model is intractable and the fact that the major objective of modeling the co-occurrence data is to predict using the conditional probability, we apply the \emph{maximum pseudo-likelihood} method to learn DEM. In consequence, the developed model and its learning method naturally avoid the above difficulties and can be easily used to compute the conditional probability in prediction. Interestingly, our method is equivalent to learning a special structured deep neural network using back-propagation and a special sampling strategy, which makes it scalable on large-scale datasets. Finally, in the experiments, we show that the DEM can achieve comparable or better results than state-of-the-art methods on datasets across several application domains

Role Of Gray Level Co-Occurrence Matrix for Convolution Neural Network Transfer Learning in Coffee Bean Classification

Abstrak— Penelitian ini membahas mengenai Peranan Gray Level Co-Occurence Matrix Untuk Convolutional Neural Network Pada Pengelompokan Biji Kopi, dengan studi kasus dari Permadi Pandansari. Data yang digunakan pada penelitian kali ini ialah data latih untuk 2 jenis biji kopi sebesar 400 citra, sedangkan data uji untuk 2 jenis biji kopi sebesar 100 citra, sehingga total keseluruhan ialah 500 citra biji kopi. Proses ektraksi yang digunakan ialah ektraksi tektstur dan warna yang didapatkan dari ekstraksi Grey Level Co-Occurrence Matrix. Dilanjut dengan metode deep learning yang digunakan untuk pengelompokan ialah Convolutional Neural Network dengan menggunakan transfer learning VGG-16. Untuk memaksimalkan hasilnya, penelitian kali ini juga menerapkan optimasi ADAM dan juga aktivasi ReLU serta Softmax. Hasil uji ekstraksi fitur ditentukan dengan nilai akurasi, presisi, recall, F1-Score dan juga Cross Validation. This study discusses the Role of Gray Level Co-Occurence Matrix for Convolutional Neural Networks in Grouping Coffee Beans, with a case study from Permadi Pandansari. The data used in this research is training data for 2 types of coffee beans of 400 images, while the test data for 2 types of coffee beans is 100 images, so the total is 500 images of coffee beans. The extraction process used is texture and color extraction obtained from the Gray Level Co-Occurrence Matrix extraction. Followed by the deep learning method used for grouping is the Convolutional Neural Network using VGG-16 transfer learning. To maximize the results, this research also applies ADAM optimization and also ReLU and Softmax activation. The results of the feature extraction test are determined by the values of accuracy, precision, recall, F1-Score and also Cross Validation.   Kata Kunci— Pengelompokan, Biji Kopi Arabika, Biji Kopi Robusta, Ekstraksi Grey Level Co-Occurrence Matrix, Convolution Neural Network, Transfer Learning VGG16.

Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demon­strated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks