Local Binary Pattern Features Research Articles

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models.

Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI. Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models. Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%. The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

Facial Expression Recognition Using a Novel Modeling of Combined Gray Local Binary Pattern

Facial Expression Recognition (FER) is an active research field at present. Deep learning is a good method that is widely used in this field but it has extreme hardware requirements and it is hard to apply in normal terminal devices. So, many other methods are being researched to apply FER in such devices and systems. This work proposes fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features in facial expression recognition to enhance the recognition rate that can apply FER in the kind of devices and systems. The work included the main steps such as the technique of cropping an input face image from a camera or dataset, the approach of dividing face images into nonoverlap regions for extracting LBP features, applying the fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features, using uniform feature to reduce the lengths of descriptors, and finally using Support Vector Machine (SVM) for emotion classification. Four popular facial emotion datasets are used in experiments and their results demonstrate that the recognition rate of the proposed method is better in comparison with two types of existent features: Local Binary Pattern (LBP) and Combined Local Binary Pattern (CLBP). The accuracy of experiments performed on four facial expression datasets with different sizes is from about 95% to more than 99%.

Hybrid Techniques for Diagnosis with WSIs for Early Detection of Cervical Cancer Based on Fusion Features

Cervical cancer is a global health problem that threatens the lives of women. Liquid-based cytology (LBC) is one of the most used techniques for diagnosing cervical cancer; converting from vitreous slides to whole-slide images (WSIs) allows images to be evaluated by artificial intelligence techniques. Because of the lack of cytologists and cytology devices, it is major to promote automated systems that receive and diagnose huge amounts of images quickly and accurately, which are useful in hospitals and clinical laboratories. This study aims to extract features in a hybrid method to obtain representative features to achieve promising results. Three proposed approaches have been applied with different methods and materials as follows: The first approach is a hybrid method called VGG-16 with SVM and GoogLeNet with SVM. The second approach is to classify the cervical abnormal cell images by ANN classifier with hybrid features extracted by the VGG-16 and GoogLeNet. A third approach is to classify the images of abnormal cervical cells by an ANN classifier with features extracted by the VGG-16 and GoogLeNet and combine them with hand-crafted features, which are extracted using Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms. Based on the mixed features of CNN with features of FCH, GLCM, and LBP (hand-crafted), the ANN classifier reached the best results for diagnosing abnormal cells of the cervix. The ANN network achieved with the hybrid features of VGG-16 and hand-crafted an accuracy of 99.4%, specificity of 100%, sensitivity of 99.35%, AUC of 99.89% and precision of 99.42%.

Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions

Site-specific weed management in Precision Agriculture is becoming a popular topic among researchers and farmers. The objective of this study was to classify weeds and crop species using RGB image texture features with the comparison of the Support Vector Machine (SVM) classification model and deep learning-based visual group geometry 16 (VGG16) classification models. A total of 3792 RGB images of crop and weed samples were captured from the greenhouse, including 2271 weed images and 1521 crop images. ReliefF feature selection algorithm was applied to select the most important features for prediction models. The SVM and VGG16 deep learning classifiers were used to classify four weeds (horseweed, kochia, ragweed, and waterhemp) and six crop species (black bean, canola, corn, flax, soybean, and sugar beets). Accuracy, f1-score, and kappa score metrics were used to evaluate model performance and data reliability. The VGG16 model classifiers had outperformed all the SVM model classifiers. The results showed that average f1-scores of the VGG16 model classifier were obtained between 93% and 97.5%. The f1-score value of 100% was obtained for the corn class in the VGG16 Weeds-Corn classifier, which seems outstanding for the corn crop production system. This study shows promising results of using a deep learning algorithm (VGG16) for weed identification in site-specific weed management in precision agriculture. • Machine learning (ML) and deep learning (DL) based classifiers were built for six types of crop production systems. • Local binary pattern and Gray Level Co-Occurrence Matrix texture features were extracted for ML. • ReliefF feature selection algorithm was used for machine learning feature engineering. • VGG16 classifiers outperformed the SVM classifiers with an f1-score value of over the 93%. • The highest f1-score value of 100% was obtained for the corn class in the Weeds-Corn VGG16 classifier.

Segmentation algorithm for overlap recognition of seedling lettuce and weeds based on SVM and image blocking

For the problem of a low recognition rate and shape feature failure caused by overlapping seedlings and weeds during the development of an intelligent lettuce weeding robot, a method to identify seedling lettuce and weeds based on an image block and support vector machine (SVM) is proposed, which realizes their precise identification and boundary segmentation. The a* channel is used to grayscale the collected image. The Otsu and morphological methods are selected to extract all the green targets in the image. The connected component analysis method is applied to label the green targets with regions of interest (ROIs), and those with pixel areas larger than the area threshold are normalized to 256 × 256 pixels. The image blocking technique is introduced to separately aliquot the normalized ROI, with block sizes of 16 × 16, 32 × 32, and 64 × 64 pixels. On this basis, the image sub-blocks are manually labeled, block by block, to extract three texture features: histogram of oriented gradient (HOG), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). With the accuracy of fivefold cross-validation as the optimization objective, a genetic algorithm (GA) is used to optimize the SVM penalty and kernel parameters of 21 groups of research objects (one block size has three texture features, which are arbitrarily combined to form seven research objects, with a total of three block sizes). We compare the recognition performance of the SVM, RF, KNN, and GA-SVM classifiers in a single feature and a combination of fusion strategies through comparative analysis. When the block size is 32 × 32 pixels, the fusion of LBP and GLCM features under the GA-SVM classifier has the highest accuracy, and the optimal SVM model for the identification of lettuce and weeds in the seedling stage is obtained. For the misidentified image sub-blocks in optimization model recognition, an image block reconstruction method based on the comparison of the center point and eight-neighbor label value is proposed, and this is combined with the proportion of image blocks of two labels for comprehensive judgment. The center point label value is reconstructed to the improve recognition accuracy. Experimental results show that the average precision, recall, and F1 score of the proposed method are 0.9473, 0.9529, and 0.9498, respectively, and those of images without overlapping leaves can all reach 1, thus providing a theoretical basis for crop recognition and segmentation.

Improved deep generative adversarial network with illuminant invariant local binary pattern features for facial expression recognition

ABSTRACT Facial Expression Recognition (FER) is one of the prevailing as well as demanding tasks in social communication. In general, face expressions are usual and straight ways for individuals to converse their intentions as well as emotions. This paper proposes the Taylor-Chicken Swarm Optimization-based Deep Generative Adversarial Network (Taylor-CSO-based Deep GAN) for FER. Here, the facial expressions are predicted through a series of steps, such as video frame extraction, pre-processing, face detection, feature extraction, as well as FER. The major contribution of the proposed work lies in the last step, where Taylor-CSO based Deep GAN is employed for recognizing facial expressions. Initially, video frames are extracted from the input video as well as pre-processing is done to extract the Region of Interest (RoI). Then, the Viola Jones algorithm is employed to detect the face images, and Illuminant Invariant Local Binary Pattern (IILBP) features are extracted. Finally, the FER is performed by the proposed model. The performance of the developed model is analyzed using the CK+ dataset, RAVDESS dataset, and SAVEE dataset. Also, the developed model’s performance is evaluated with conventional FER methods. The performance analysis exhibits that the adopted model is precise and valuable.

Identical Image Extraction from PDF Document Using LBP (Local Binary Patterns) and RGB (Red, Green and Blue) Color Features

Abstract: Content based image retrieval (CBIR) has become a main research area in multimedia applications. In the literature, there is lot of papers focusing on the content-based image retrieval in order to extract the semantic information within the query concept. The content based image retrieval aims to find the similar images from PDF document against a query image. Generally, the similarity between the representative features of the query image and PDF images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. In this project we have developed a method to extract the images present in PDF documents based on appropriate features extracted from query image such as color and local binary pattern features to find perfect matching image is present in PDF or not. Based on similarity analysis model we will display the query input image is present in PDF documents or not. If query matching image is present in the PDF documents then we will display the list corresponding PDF documents using suitable applications to show the presence.

New Local Binary Pattern Feature Extractor with Adaptive Threshold for Face Recognition Applications

This paper represents a feature extraction method constructed on the local binary pattern (LBP) structure. The proposed method introduces a new adaptive thresholding function to the LBP method replacing the fixed thresholding at zero. The introduced function is a Gaussian Distribution Function (GDF) variation. The proposed technique uses the global and local information of the image and image blocks to perform the adaptation. The adaptive function adds to the on-hand im-age’s features by preserving the information of the amplitude of the pixel difference rather than just considering the sign of the pixel difference in the process of LBP coding. This feature improves the accuracy of the face recognition system by providing additional information. The proposed method demonstrates a higher recognition rate than other presented techniques (%97.75). The proposed method was also tested with different types of noise to demonstrate its effectiveness in the presence of various levels of noise. The Extended Yale B dataset was used for the testing along with Support Vector Machine (SVM) as classifier.

Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification.

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.

Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

IntroductionReliable computer diagnosis of diabetic retinopathy (DR) is needed to rescue many with diabetes who may be under threat of blindness. This research aims to detect the presence of diabetic retinopathy in fundus images and grade the disease severity without lesion segmentation.MethodsTo ensure that the fundus images are in a standard state of brightness, a series of preprocessing steps have been applied to the green channel image using histogram matching and a median filter. Then, contrast-limited adaptive histogram equalisation is performed, followed by the unsharp filter. The preprocessed image is divided into small blocks, and then each block is processed to extract uniform local binary patterns (LBPs) features. The extracted features are encoded, and the feature size is reduced to 3.5 percent of its original size. Classifiers like Support Vector Machine (SVM) and a proposed CNN model were used to classify retinal fundus images. The classification is abnormal or normal and to grade the severity of DR.ResultsOur feature extraction method was tested on a binary classifier and resulted in an accuracy of 98.37% and 98.84% on the Messidor2 and EyePACS databases, respectively. The proposed system could grade DR severity into three grades (0: no DR, 1: mild DR, and 5: moderate, severe NPDR, and PDR). It obtains an F1-score of 0.9617 and an accuracy of 95.37% on the EyePACS database, and an F1-score of 0.9860 and an accuracy of 97.57% on the Messidor2 database. The resultant values are dependent on the selection of (neighbours, radius) pairs during the extraction of LBP features.ConclusionsThis study’s results proved that the preprocessing steps are significant and had a great effect on highlighting image features. The novel method of stacking and encoding the LBP values in the feature vector greatly affects results when using SVM or CNN for classification. The proposed system outperforms the state of the artwork. The proposed CNN model performs better than SVM.

Facial expression recognition using HOG and LBP features with convolutional neural network

In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is applied to a convolutional neural network (CNN). The architecture of this CNN constitutes three convolutional layers and three max-pooling layers. The output layers involve BatchNormalization, three dense layers, and two dropout layers. The proposed architecture is validated on the extended cohn-kanade dataset (CK+). We obtain improvement in the accuracy of the CNN model from 0.9593 to 0.967 and 0.975 after using the LBP and HOG respectively.

Ensemble of features for efficient classification of high-resolution remote sensing image

ABSTRACT Extracting feature is one of the important methods in classification of high-resolution remote sensing image. A good feature set can result in an efficient classification process. Recent trend moves in extracting the features from the image using neural networks with no human intervention. Our approach uses the deep convolutional neural network for extracting deep features. To still rise the efficiency of the extracted features, the proposed system combines the deep features with other features like Gabor features and novel reformed local binary pattern features. The features are combined and sent for classification. Then, the classification process is done to classify the images. The proposed system introduces two novel ideas, in its feature extraction implementation, namely (1) initialisation of filter values for the CNN and (2) change in local binary pattern feature extraction process. The experimental results are carried out with LISS IV Madurai image, and evaluation is done for the verification of the results. It is found that the system proposed produces good results when compared with other existing methods.

Botanical Leaf Disease Detection and Classification Using Convolutional Neural Network: A Hybrid Metaheuristic Enabled Approach

Botanical plants suffer from several types of diseases that must be identified early to improve the production of fruits and vegetables. Mango fruit is one of the most popular and desirable fruits worldwide due to its taste and richness in vitamins. However, plant diseases also affect these plants’ production and quality. This study proposes a convolutional neural network (CNN)-based metaheuristic approach for disease diagnosis and detection. The proposed approach involves preprocessing, image segmentation, feature extraction, and disease classification. First, the image of mango leaves is enhanced using histogram equalization and contrast enhancement. Then, a geometric mean-based neutrosophic with a fuzzy c-means method is used for segmentation. Next, the essential features are retrieved from the segmented images, including the Upgraded Local Binary Pattern (ULBP), color, and pixel features. Finally, these features are given into the disease detection phase, which is modeled using a Convolutional Neural Network (CNN) (deep learning model). Furthermore, to enhance the classification accuracy of CNN, the weights are fine-tuned using a new hybrid optimization model referred to as Cat Swarm Updated Black Widow Model (CSUBW). The new hybrid optimization model is developed by hybridizing the standard Cat Swarm Optimization Algorithm (CSO) and Black Widow Optimization Algorithm (BWO). Finally, a performance evaluation is undergone to validate the efficiency of the projected model.

An Automated System for the Classification of COVID-19, Suspected COVID-19 and Healthy Lung CT Images based on Local Binary Pattern and Deep Learning Features

Because of the inadequate capacity and a substantial surge of probable COVID-19 cases, several health systems around worldwide have collapsed. As a result, the requirement for a rapid, effective, and precise way to reduce radiologists' workload in diagnosing suspected instances has arisen. The goal of the present study is to develop a novel system to automatically diagnose and classify lung CT scans into three categories: suspected covid-19, covid-19, and healthy lung scans. Before feature extraction using convolutional neural network (CNN) and Local Binary Pattern (LBP) approaches, the CT scans are first pre-processed through implementing a set of algorithms. Lastly, with the use of the support vector machine (SVM) model, such features are divided into three groups. The maximum accuracy attained in classifying a dataset of 351 CT scans of the lungs was 98.22%. The outcomes of the experiments show that merging the extracted features increases the effectiveness of lung classification CT scans.

COMPLETED LOCAL BINARY PATTERN WITH RANDOM FOREST ON IMAGE CLASSIFICATION

The work develops effective machine learning models for image classification. The work also discusses the performance of random forest with completed local binary pattern feature extraction. Texture feature extraction methods, such as completed local Binary Pattern, can be used to address classification problems successfully

Feature Generalization for Breast Cancer Detection in Histopathological Images.

Recent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient and lightweight compared to deep architecture-based transfer learning techniques. This research has identified the competence of classifying breast cancer images using feature engineering and representation learning over the established and contemporary notion of using transfer learning techniques. Moreover, it has revealed superior feature learning capacity with feature fusion in contrast to the conventional belief of understanding unknown feature patterns better with representation learning alone. Experiments have been conducted on two different and popular breast cancer image datasets, namely, KIMIA Path960 and BreakHis datasets. A comparison of image-level accuracy is performed on these datasets using the above-mentioned feature extraction techniques. Image level accuracy of 97.81% is achieved for KIMIA Path960 dataset using individual features extracted with handcrafted (color histogram) technique. Fusion of uniform Local Binary Pattern (uLBP) and color histogram features has resulted in 99.17% of highest accuracy for the same dataset. Experimentation with BreakHis dataset has resulted in highest classification accuracy of 88.41% with color histogram features for images with 200X magnification factor. Finally, the results are contrasted to that of state-of-the-art and superior performances are observed on many occasions with the proposed fusion-based techniques. In case of BreakHis dataset, the highest accuracies 87.60% (with least standard deviation) and 85.77% are recorded for 200X and 400X magnification factors, respectively, and the results for the aforesaid magnification factors of images have exceeded the state-of-the-art.

Face recognition algorithm based on stack denoising and self-encoding LBP

Abstract To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.

Completed local binary patterns feature integrated convolutional neural network-based terrain classification algorithm in polarimetric synthetic aperture radar images

We propose a polarimetric synthetic aperture radar (PolSAR) image terrain classification algorithm based on complete local binary patterns (CLBP) feature integrated convolutional neural network (CNN) (CLBP-CNN). Traditional CNN has a powerful high-level deep features extraction ability, which can effectively improve the terrain classification accuracy in PolSAR images. However, most traditional CNN-based methods only focus on the high-level deep feature extraction of the synthetic aperture radar (SAR) terrains; they ignore the low-level texture features, resulting in incomplete feature representation and poor classification accuracy. In fact, low-level texture features also play an important role in PolSAR terrain classification. To solve the problem that traditional CNN-based terrain classification methods easily lose the low-level texture features in the process of feature extraction, the proposed method uses the CLBP descriptor to extract multi-level texture features under different receptive fields, and it adaptively combines the high-level deep features and the low-level texture features for better SAR terrain feature description. CLBP-CNN greatly alleviates the shortcomings of traditional CNN in missing the low-level texture features; it improves the feature representation completeness, so it can achieve better terrain classification results. The superiority of CLBP-CNN is verified on the data sets of Flevoland, San Francisco, and Oberpfaffenhofen.

An Objective Classification Approach of Cacao Pods using Local Binary Pattern Features and Artificial Neural Network Architecture (ANN)

Objectives: Cacao is considered the “food of the gods” and is one of the leading crops of the tropical world. The differentiated use for both food and nonfood of cacao is the reason why it has been gaining recognition around the world. Despite this, cacao production is said to be declining for a variety of reasons. One of these is the possible contamination among the harvested pods due to the manual way of performing classification and segregation of cacao pods during the harvest period. Traditional approach or the manual way of classifying whether the pod is healthy or not is very subjective, which may give erroneous results, and mixing of healthy and unhealthy pods may lead to contamination once they are transported and stored. Hence, we develop a study to make an objective approach that performs cacao pods classification by discriminating healthy cacao pods from unhealthy ones using Artificial Neural Network (ANN). Methods: This study presents a Cacao Pod Classification System that will automatically classify cacao pods (i.e. healthy or not healthy) during the harvest period. We leverage imaging technology and machine learning techniques to create a classifier that performs binomial classification. Color Histogram (CH) and Local Binary Pattern (LBP) features were used as input to the Artificial Neural Network (ANN) classifier. Findings: Experiments reveal that the approach successfully extracts features from the captured images of cacao pods and provides efficient results in terms of the four performance measures (i.e. accuracy, precision, recall, and f1-score) giving an accuracy rate of 98.3% in particular, which is superior among other classifiers tested such as the Support Vector Machine (SVM) and Logistic Regression (LR). Novelty: Artificial Neural Network classifier was found to be superior from other classifiers tested in classifying healthy and unhealthy cacao pods along with color histogram and local binary pattern as features used in the study. Application: The pilot test of the application was performed in a 5-hectare privately owned cacao farm situated at Poblacion, Initao, Misamis Oriental, Philippines. Keywords: Image Processing; artificial neural network; cacao pod; classification; feature extraction; segmentation