Identifying of unripe Ambon and Hijau banana fruits using computer vision and extreme learning machine classifier

The unripe Indonesian cultivar bananas of ambon kuning (Ambon) and ambon hijau (Hijau) after harvesting show a very close looking, green colour, similar size and shape, even Ambon one is costly than the Hijau. Hence in this study, identification was conducted using computer vision utilizing banana finger image taken with a mobile phone camera. The feature used as a differentiating feature is the shape feature and the skin texture feature of the fruit. The shape features were then extracted using morphological descriptor and convex hull, while the texture features were extracted using local binary pattern (LBP). The extreme learning machine (ELM) classifier was used to recognize both cultivars. A total of 76 banana finger imagery data were used in 3-fold testing. The test results showed that the combined use of shape and LBP features resulted in the highest accuracy, precision and recall values more than 93%. These results showed that the combination of the two features can effectively be used to distinguish the unripe Ambon and Hijau bananas.

Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors

The principal intention of this paper is to study face recognition across age progression at two levels: feature extraction and classification. In other words, this work aims to prove the benefit of replacing the Softmax layer of the Deep-Convolutional Neural Networks (CNN) by Extreme Learning Machine (ELM) classifier based on deep features computed from fully-connected layer of pre-trained AlexNet CNN model, in a context of age-invariant face recognition. Experimental results indicate that the ELM classifier combined with feature extracted by the pre-trained AlexNet CNN model worked effectively for face recognition across age progression. As significant highest mean accuracy rates are always obtained using ELM classifier. These results are more significant, following a 95% confidence level hypothesis test.

Deep Convolutional Neural Network Based Extreme Learning Machine Image Classification

In this paper, we introduce a new deep convolutional neural network based extreme learning machine model for the classification task in order to improve the network's performance. The proposed model has two stages: first, the input images are fed into a convolutional neural network layer to extract deep-learned attributes, and then the input is classified using an ELM classifier. The proposed model achieves good recognition accuracy while reducing computational time on both the MNIST and CIFAR-10 benchmark datasets.

An Effective Turkey Marble Classification System: Convolutional Neural Network with Genetic Algorithm -Wavelet Kernel - Extreme Learning Machine

Marble is one of the most popular decorative elements. Marble quality varies depending on its vein patterns and color, which are the two most important factors affecting marble quality and class. The manual classification of marbles is likely to lead to various mistakes due to different optical illusions. However, computer vision minimizes these mistakes thanks to artificial intelligence and machine learning. The present study proposes the Convolutional Neural Network- (CNN-) with genetic algorithm- (GA) Wavelet Kernel- (WK-) Extreme Learning Machine (ELM) (CNN–GA-WK-ELM) approach. Using CNN architectures such as AlexNet, VGG-19, SqueezeNet, and ResNet-50, the proposed approach obtained 4 different feature vectors from 10 different marble images. Later, Genetic Algorithm (GA) was used to optimize adjustable parameters, i.e. k, 1, and m, and hidden layer neuron number in Wavelet Kernel (WK) – Extreme Learning Machine (ELM) and to increase the performance of ELM. Finally, 4 different feature vector parameters were optimized and classified using the WK-ELM classifier. The proposed CNN–GA-WK-ELM yielded an accuracy rate of 98.20%, 96.40%, 96.20%, and 95.60% using AlexNet, SequeezeNet, VGG-19, and ResNet-50, respectively.

Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

A Novel Region-Extreme Convolutional Neural Network for Melanoma Malignancy Recognition

Melanoma malignancy recognition is a challenging task due to the existence of intraclass similarity, natural or clinical artefacts, skin contrast variation, and higher visual similarity among the normal or melanoma-affected skin. To overcome these problems, we propose a novel solution by leveraging “region-extreme convolutional neural network” for melanoma malignancy recognition as malignant or benign. Recent works on melanoma malignancy recognition employed the traditional machine learning techniques based on various handcrafted features or the recently introduced CNN network. However, the efficient training of these models is possible, if they localize the melanoma affected region and learn high-level feature representation from melanoma lesion to predict melanoma malignancy. In this paper, we incorporate this observation and propose a novel “region-extreme convolutional neural network” for melanoma malignancy recognition. Our proposed region-extreme convolutional neural network refines dermoscopy images to eliminate natural or clinical artefacts, localizes melanoma affected region, and defines precise boundary around the melanoma lesion. The defined melanoma lesion is used to generate deep feature maps for model learning using the extreme learning machine (ELM) classifier. The proposed model is evaluated on two challenge datasets (ISIC-2016 and ISIC-2017) and performs better than ISIC challenge winners. Our region-extreme convolutional neural network recognizes the melanoma malignancy 85% on ISIC-2016 and 93% on ISIC-2017 datasets. Our region-extreme convolutional neural network precisely segments the melanoma lesion with an average Jaccard index of 0.93 and Dice score of 0.94. The region-extreme convolutional neural network has several advantages: it eliminates the clinical and natural artefacts from dermoscopic images, precisely localizes and segments the melanoma lesion, and improves the melanoma malignancy recognition through feedforward model learning. The region-extreme convolutional neural network achieves significant performance improvement over existing methods that makes it adaptable for solving complex medical image analysis problems.

Expanded and Filtered Features based ELM Model for Thyroid Disease Classification

Thyroid disorder affects the regulation of various metabolic processes throughout the human body. The structural and functional disorders can affect the body as well as the brain. The computer-aided diagnosis system can identify the kind of Thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. In this paper, a fuzzy adaptive feature filtration, expansion, and again filtration based model is presented for generating the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multi-measure evaluation. At the first level, the composite-fuzzy measures are combined with expert’s recommendations for identifying the ranked and relevant features. At the second level, the statistical computation based distance measure is applied for expanding the featureset. The fuzzification is applied to expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the ELM classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the NaiveBayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and RBF Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy based parameters. The proposed framework clams the significant gain in accuracy, relevancy, and reduction in the error rate.

Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine

Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms.

Multi-Type Classification Comparison of Mammogram Abnormalities

Cancer is a life-threatening disease which reduces the lifespan of humans. If the disease is treated early, the lifespan can be extended. This paper provides a useful method for detecting the abnormalities in the mammograms. The proposed method uses four phases such as pre-processing, segmentation, feature extraction and classification. In the pre-processing phase, median filter is utilized to enhance the quality of an image. The pre-processed image is then segmented by fuzzy C means (FCM). Three different features such as Gaussian–Hermite moments (GHM), Jacobi moments and pseudo Zernike moments (PZM) are extracted from the segmented image. Finally, extreme learning machine (ELM) classifier identifies the normal, malignant and benign kinds of cancer. This method is compared with four different classifiers. The proposed method is tested on mammographic image analysis society (MIAS) dataset and the performance is evaluated against several analogous approaches in terms of accuracy, sensitivity and specificity. The proposed approach substantially provides the best result.