scholarly journals DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment

2020 ◽  
Vol 21 (16) ◽  
pp. 5710
Author(s):  
Xiao Wang ◽  
Yinping Jin ◽  
Qiuwen Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Protein Localization ◽  
Mitochondrial Protein ◽  
Intermembrane Space ◽  
The Matrix ◽  
Localization Predictor ◽  
High Level ◽  
Fold Cross Validation

Mitochondrial proteins are physiologically active in different compartments, and their abnormal location will trigger the pathogenesis of human mitochondrial pathologies. Correctly identifying submitochondrial locations can provide information for disease pathogenesis and drug design. A mitochondrion has four submitochondrial compartments, the matrix, the outer membrane, the inner membrane, and the intermembrane space, but various existing studies ignored the intermembrane space. The majority of researchers used traditional machine learning methods for predicting mitochondrial protein localization. Those predictors required expert-level knowledge of biology to be encoded as features rather than allowing the underlying predictor to extract features through a data-driven procedure. Besides, few researchers have considered the imbalance in datasets. In this paper, we propose a novel end-to-end predictor employing deep neural networks, DeepPred-SubMito, for protein submitochondrial location prediction. First, we utilize random over-sampling to decrease the influence caused by unbalanced datasets. Next, we train a multi-channel bilayer convolutional neural network for multiple subsequences to learn high-level features. Third, the prediction result is outputted through the fully connected layer. The performance of the predictor is measured by 10-fold cross-validation and 5-fold cross-validation on the SM424-18 dataset and the SubMitoPred dataset, respectively. Experimental results show that the predictor outperforms state-of-the-art predictors. In addition, the prediction of results in the M983 dataset also confirmed its effectiveness in predicting submitochondrial locations.

scholarly journals Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020 ◽  
Vol 10 (6) ◽  
pp. 1999 ◽  
Author(s):  
Milica M. Badža ◽  
Marko Č. Barjaktarović
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Magnetic Resonance Images ◽  
Generalization Capability ◽  
Data Set ◽  
Fold Cross Validation

The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

scholarly journals User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1158
Author(s):  
Seung-Min Park ◽  
Hong-Gi Yeom ◽  
Kwee-Bo Sim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Cross Validation ◽  
Brain Connectivity ◽  
Visually Evoked Potentials ◽  
State Classification ◽  
Sensorimotor Rhythms ◽  
The Brain ◽  
Fold Cross Validation

The brain–computer interface (BCI) is a promising technology where a user controls a robot or computer by thinking with no movement. There are several underlying principles to implement BCI, such as sensorimotor rhythms, P300, steady-state visually evoked potentials, and directional tuning. Generally, different principles are applied to BCI depending on the application, because strengths and weaknesses vary according to each BCI method. Therefore, BCI should be able to predict a user state to apply suitable principles to the system. This study measured electroencephalography signals in four states (resting, speech imagery, leg-motor imagery, and hand-motor imagery) from 10 healthy subjects. Mutual information from 64 channels was calculated as brain connectivity. We used a convolutional neural network to predict a user state, where brain connectivity was the network input. We applied five-fold cross-validation to evaluate the proposed method. Mean accuracy for user state classification was 88.25 ± 2.34%. This implies that the system can change the BCI principle using brain connectivity. Thus, a BCI user can control various applications according to their intentions.

scholarly journals Classification of Traffic Vehicle Density Using Deep Learning

2020 ◽  
Vol 14 (1) ◽  
pp. 69
Author(s):  
Abdul Kholik ◽  
Agus Harjoko ◽  
Wahyono Wahyono
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Classification Model ◽  
Density Level ◽  
Vehicle Density ◽  
The Impact ◽  
Fold Cross Validation

The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data.

scholarly journals Fused Convolutional Neural Network for Facial Expression Recognition

2019 ◽  
Vol 8 (12S2) ◽  
pp. 528-532
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Expression Recognition ◽  
Optimal Learning ◽  
Group Of Seven ◽  
Fold Cross Validation

This study aims to find the optimal learning algorithm parameter, model and connection, initialization weight and normalization method using fused Convolutional Neural Network (CNN) for facial expression recognition. The best model and parameters are identified using a ten-fold cross validation method. By determining these ideal elements, a superior accuracy can potentially be achieved. CNN was utilized to a group of seven emotions from various facial expressions, namely, happy, sad, angry, surprise, disgust, fear and neutral. The four layer CNN configuration was prepared with the JAFFE dataset, and yielded an overall accuracy of 83.72%. The outcome demonstrates that the fused CNN with the mentioned aims can generate higher accuracy with a smaller network compared to related models.

scholarly journals Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network

Techno Com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 166-174
Author(s):  
Mohammad Farid Naufal ◽  
Solichul Huda ◽  
Aryo Budilaksono ◽  
Wisnu Aria Yustisia ◽  
Astri Agustina Arius ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Fully Connected ◽  
Fold Cross Validation

Permainan batu, gunting, dan kertas sangat populer di seluruh dunia. Permainan ini biasanya dimainkan saat sedang berkumpul untuk mengundi ataupun hanya bermain untuk mengetahui yang menang dan yang kalah. Namun, perkembangan zaman dan teknologi mengakibatkan orang dapat berkumpul secara virtual. Untuk bisa melakukan permainan ini secara virtual, penelitian ini membuat model klasifikasi citra untuk membedakan objek tangan yang menunjuk batu, kertas, dan gunting. Performa metode klasifikasi merupakan hal yang harus diperhatikan dalam kasus ini. Salah satu metode klasifikasi citra yang populer adalah Convolutional Neural Network (CNN). CNN adalah salah satu jenis neural network yang biasa digunakan pada data klasifikasi citra. CNN terinspirasi dari jaringan syaraf manusia. Algoritma ini memiliki 3 tahapan yang dipakai, yaitu convolutional layer, pooling layer, dan fully connected layer. Uji coba 5-Fold cross validation klasifikasi objek tangan yang menunjuk citra batu, kertas, dan gunting menggunakan CNN pada penelitian ini menghasilkan rata-rata akurasi sebesar 97.66%.

scholarly journals Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation

2020 ◽  
Vol 4 (1) ◽  
pp. 45-51
Author(s):  
Ari Peryanto ◽  
Anton Yudhana ◽  
Rusydi Umar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Image Recognition ◽  
Cross Validation ◽  
Recognition System ◽  
Multi Layer Perceptron ◽  
Average Accuracy ◽  
Python Programming ◽  
Fold Cross Validation

Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

scholarly journals "LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia

2020 ◽  
Author(s):  
Luis H. S. Vogado ◽  
Rodrigo M. S. Veras ◽  
Kelson R. T. Aires
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Data Augmentation ◽  
Fine Tuning ◽  
Current State ◽  
Leukemia Diagnosis ◽  
New Guidelines ◽  
Texture Contrast ◽  
Tuning Methods

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.

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