Convolutional Neural Network Classification Method Research Articles

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

A design and evaluation of wearable p300 brain-computer interface system based on Hololens2

Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.

Implementasi Metode Convolutional Neural Network Pada Pengenalan Aksara Bali Berbasis Game Edukasi

Balinese script or also known as hanacaraka, is the writing used by Balinese people to write their language. In general, this script is used to write everyday language and literary language. Balinese script in the past was not only used for writing literature or sacred texts but also for writing everyday language. Balinese script plays an important role in literary writing. The sacred text of the Vedas uses Balinese script in the Sanskrit language. In preserving the Balinese script, itself, Balinese script lessons are mandatory for students from elementary school to high school. In addition to studying at school, interesting learning is certainly needed to attract students' interest. One way is by way of game applications or educational games. This Balinese script recognition application receives input in the form of Balinese script writing characters from the user, then it will be processed by preprocessing and continued with the classification training process using the Convolutional Neural Network (CNN) and Backpropagation methods. The result is a web-based application that can recognize Balinese script writing with the CNN classification method with an accuracy rate of 81.3% and gets a positive response from respondents who have tested the application.

A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System

Deep learning has been used to improve intelligent transportation systems (ITS) by classifying ship targets in interior waterways. Researchers have created numerous classification methods, but they have low accuracy and misclassify other ship targets. As a result, more research into ship classification is required to avoid inland waterway collisions. We present a new convolutional neural network classification method for inland waterways that can classify the five major ship types: cargo, military, carrier, cruise, and tanker. This method can also be used for other ship classes. The proposed method consists of four phases for the boosting of classification accuracy for Intelligent Transport Systems (ITS) based on convolutional neural networks (CNNs); efficient augmentation method, the hyper-parameter optimization (HPO) technique for optimum CNN model parameter selection, transfer learning, and ensemble learning are suggested. All experiments used Kaggle’s public Game of Deep Learning Ship dataset. In addition, the proposed ship classification achieved 98.38% detection rates and 97.43% F1 scores. Our suggested classification technique was also evaluated on the MARVEL dataset. This dataset includes 10,000 image samples for each class and 26 types of ships for generalization. The suggested method also delivered an excellent performance compared to other algorithms, with performance metrics with an accuracy of 97.04%, a precision of 96.1%, a recall of 95.92%, a specificity of 96.55%, and a 96.31% F1 score.

Aplikasi Penerjemah Bahasa Isyarat Indonesia Ke Dalam Huruf Abjad

Sign language is the language used by people with limited speech or hearing. However, not everyone knows how to translate sign language in letters. Therefore, this research is motivated because there are many people who are normal or who do not have disabilities in speaking or listening who do not know how to communicate with people who have these limitations. So from this i raised a topic for the final project, "Application of Indonesian Sign Language Into Alphabet Letters. By using the Convolutional Neural Network classification method using the MobilenetV2 model architecture. The results of the classification will be exported into a model which will then be processed using Tensorflow Lite Android Studio. Based on the results of model testing, the resulting accuracy level reaches 0.9995 with a loss value of 0.6982. So that the performance of the application detection is not optimal. In addition, this application also displays animated letters of the Indonesian sign language alphabet so that people can also learn Indonesian sign language in visual form.

Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network.

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

Self-adaptive Entropy Weighted Decision Fusion Method for Ship Image Classification Based on Multi-scale Convolutional Neural Network

Considering the limitation of single scale Convolutional Neural Network (CNN) for ship image classification, a self-adaptive entropy weighted decision fusion method for ship image classification based on multi-scale CNN is proposed. Firstly, the multi-scale CNN is used to extract the multi-scale features of ship image with different sizes, and the optimum models of different sub-networks are trained. Then, the ship images of test set are tested on the optimum models, and the probability value that is output by Softmax function of multi-scale CNN is obtained, which is used to calculate the information entropy so as to realize the adaptive weight assigned to different input ship images. Finally, self-adaptive entropy weighted decision fusion is carried out for the probability value that is output by Softmax function of different sub-networks to realize the final ship image classification. Experiments perform on VAIS (Visible And Infrared Spectrums) and self-built datasets respectively, and the proposed method achieves average accuracy of 95.07% and 97.50% on these datasets respectively. The experimental results show that the proposed method has better classification performance than those of the single scale CNN classification method and other state-of-the-art methods.

Fault diagnosis of the bushing infrared images based on mask R‐CNN and improved PCNN joint algorithm

Bushings are served as an important component of the power transformers; it's of great significance to keep the bushings in good insulation condition. The infrared images of the bushing are proposed to diagnose the fault with the combination of image segmentation and deep learning, including object detection, fault region extraction, and fault diagnosis. By building an object detection system with the frame of Mask Region convolutional neural network (CNN), the bushing frame can be exactly extracted. To distinguish the fault region of bushings and the background, a simple linear iterative clustering-based pulse coupled neural network is proposed to improve the fault region segmentation performance. Then, two infrared image feature parameters, the relative position and area, are explored to classify fault type effectively based on the K-means cluster technique. With the proposed joint algorithm on bushing infrared images, the accuracy reaches 98%, compared with 44% by the conventional CNN classification method. The integrated algorithm provides a feasible and advantageous solution for the field application of bushing image-based diagnosis.

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals.

A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

Autism AI: a New Autism Screening System Based on Artificial Intelligence

Autistic spectrum disorder (ASD) is a neurodevelopment condition normally linked with substantial healthcare costs and time-consuming assessments where early detection of ASD traits can help limit the development of the condition. The existing conventional ASD screening methods contain a large number of items and are based on domain expert rules which may be criticized of being lengthy and subjective. More importantly, these methods use basic scoring functions to pinpoint to autistic traits rather intelligently learning patterns from cases and controls which can be more accurate and efficient. One promising solution to deal with the above issues and speed up ASD assessment referrals is to develop intelligent artificial intelligence screening methods that not only provide accurate pre-diagnostic classifications but also improve the efficiency and accessibility of the screening process. This paper proposes a new autism screening system that replaces the conventional scoring functions in classic screening methods with deep learning algorithms. The system is composed of a mobile application that provides the user interface capturing questionnaire data; an intelligent ASD detection web service that interfaces with a Convolutional Neural Network (CNN) trained with historical ASD cases; and a database that enables the CNN to learn new knowledge from future users of the system. The CNN classification method was evaluated against a large autism dataset consisting of adult, adolescent, child, and toddler cases and controls. The results obtained from the CNN were compared with other intelligent algorithms in which superior performance was achieved by the CNN. Particularly, the proposed CNN-based ASD classification system revealed higher accuracy, sensitivity, and specificity when compared with conventional screening methods. This indeed will be of high benefit for busy medical clinics and diagnosticians and could possibly be a new direction to change the way ASD diagnosis process is conducted in the future.

Inception v3 based cervical cell classification combined with artificially extracted features

Traditional cell classification methods generally extract multiple features of the cell manually. Moreover, the simple use of artificial feature extraction methods has low universality. For example, it is unsuitable for cervical cell recognition because of the complexity of the cervical cell texture and the large individual differences between cells. Using the convolutional neural network classification method is a good way to solve this problem. However, although the cell features can be extracted automatically, the cervical cell domain knowledge will be lost, and the corresponding features of different cell types will be missing; hence, the classification effect is not sufficiently accurate. Aiming at addressing the limitations of the two mentioned classification methods, this paper proposes a cell classification algorithm that combines Inception v3 and artificial features, which effectively improves the accuracy of cervical cell recognition. In addition, to address the under-fitting problem and carry out effective deep learning training with a relatively small amount of medical data, this paper inherits the strong learning ability from transfer learning, and achieves accurate and effective cervical cell image classification based on the Herlev dataset. Using this method, an accuracy of more than 98% is achieved, providing an effective framework for computer aided diagnosis of cervical cancer. The proposed algorithm has good universality, low complexity, and high accuracy, rendering it suitable for further extension and application to the classification of other types of cancer cells.

Multi-spectral remote sensing images feature coverage classification based on improved convolutional neural network.

With the continuous development of the earth observation technology, the spatial resolution of remote sensing images is also continuously improved. As one of the key problems in remote sensing images interpretation, the classification of high-resolution remote sensing images has been widely concerned by scholars at home and abroad. With the improvement of science and technology, deep learning has provided new ideas for the development of image classification, but it has not been widely used in remote sensing images processing. In the background of remote sensing huge data, the remote sensing images classification based on deep learning proposed in the study has more research significance and application value. The study proposes a high-resolution remote sensing images classification method based on an improved convolutional neural network. The traditional convolutional neural network framework is optimized and the initial structure is added. The actual classification results of radial basis functions and support vector machine are compared horizontally. The classification results of hyperspectral images were presented that the improved method can perform better in overall accuracy and Kappa coefficient. The commission errors of support vector machine classification method are more than 6 times of that of the improved convolutional neural network classification method and the overall accuracy of the improved convolutional neural network classification method has reached 97% above.

A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

Deep learning (DL) is a hot topic in current pattern recognition and machine learning. DL has unprecedented potential to solve many complex machine learning problems and is clearly attractive in the framework of mobile devices. The availability of powerful pattern recognition tools creates tremendous opportunities for next-generation smart applications. A convolutional neural network (CNN) enables data-driven learning and extraction of highly representative, hierarchical image features from appropriate training data. However, for some data sets, the CNN classification method needs adjustments in its structure and parameters. Mobile computing has certain requirements for running time and network weight of the neural network. In this paper, we first design an image processing module for a mobile device based on the characteristics of a CNN. Then, we describe how to use the mobile to collect data, process the data, and construct the data set. Finally, considering the computing environment and data characteristics of mobile devices, we propose a lightweight network structure for optical character recognition (OCR) on specific data sets. The proposed method using a CNN has been validated by comparison with the results of existing methods, used for optical character recognition.

Leukocyte recognition with convolutional neural network

Accurate segmentation of leukocytes is a primary and very difficult problem because of the non-uniform color, uneven illumination of blood smear image. An improved algorithm based on feature weight adaptive K-means clustering for extracting complex leukocytes is proposed. In this paper, the initial clustering center is chosen according to the histogram distribution of a cell image; this approach not only improves the clustering effect but also reduces the time complexity of the algorithm from O (n) to O (1). Prior to white blood cell extraction, the color space is decomposed. Then, color space decomposition and K-means clustering are combined for image segmentation. And then adherent complex white blood cells are separated again based on watershed algorithm. Finally, classification experiments based on convolutional neural network were performed and compared with other methods; 368 representative images were used to evaluate the performance of our method. The proposed segmentation method achieves 95.81% segmentation accuracy. The classification accuracy reached a maximum of 98.96%, and the average classification time is 0.39 s. Compared with those in the existing algorithms for WBC, convolutional neural network classification method not only presents obvious advantages but can also be easily improved.

3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT.

Identification of pre-invasive lesions (PILs) and invasive adenocarcinomas (IACs) can facilitate treatment selection. This study aimed to develop an automatic classification framework based on a 3D convolutional neural network (CNN) to distinguish different types of lung cancer using computed tomography (CT) data. The CT data of 1,545 patients suffering from pre-invasive or invasive lung cancer were collected from Fudan University Shanghai Cancer Center. All of the data were preprocessed through lung mask extraction and 3D reconstruction to adapt to different imaging scanners or protocols. The general flow for the classification framework consisted of nodule detection and cancer classification. The performance of our classification algorithm was evaluated using a receiver operating characteristic (ROC) analysis, with diagnostic results from three experienced radiologists. The sensitivity, specificity, accuracy, and AUC (area under the ROC curve) values of our proposed automatic classification method were 88.5%, 80.1%, 84.0%, and 89.2%, respectively. The results of the CNN classification method were compared to those of three experienced radiologists. The AUC value of our method (AUC =0.892) was higher than those of all radiologists (radiologist 1: 80.5%; radiologist 2: 83.9%; and radiologist 3: 86.7%). The 3D CNN-based classification algorithm is a promising tool for the diagnosis of pre-invasive and invasive lung cancer and for the treatment choice decision.