A Hybrid Convolutional Neural Network and Grid Extrapolation Approach for Predicting Solutions of the Electronic Schrödinger Equation

Chromosomes are thread-like structures located in the cell nucleus that contains the human body blueprint. Chromosome analysis is also known as karyotyping is the test taken to detect the abnormalities identified in the human chromosome. The two types of widely known chromosome abnormalities are structural and numerical abnormalities. Manual karyotyping is complex, time-consuming, and error-prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the deep convolutional neural network (DCNN) architecture. Training the DCNN architecture from scratch needs a huge dataset and to overcome this problem a generative adversarial networks is used to create adversarial samples that resemble the images in the actual dataset. The time-consuming hyperparameter tuning in the DCNN architecture is overcome using the hybrid moth-flame optimization integrated with the hill-climbing strategy (HMFOHC). The HMFOHC algorithm is mainly utilized in this article to minimize the huge number of parameters associated with the DCNN architecture. The efficiency of the proposed methodology is evaluated using two datasets namely the BioImLab chromosome dataset and hospital dataset. The proposed HMFOHC optimized DCNN architecture is mainly utilized for multiclass classification where it differentiates five numerical chromosome abnormalities, namely Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X. The proposed model offers an accuracy, F1-score, and kappa coefficient value of 98.65%, 98.86%, and 0.9894, respectively. The results obtained show that the proposed model achieves higher classification accuracy when compared with the different state-of-art techniques such as deep learning, random forest, and CNN. The inference time of the proposed methodology is 12.5s which is relatively lower than the state-of-art techniques. The proposed approach can help cytogenetics forensic experts make better decisions and save time by automating manual karyotyping. RESEARCH HIGHLIGHTS: Manual karyotyping is complex, time-consuming, and error-prone. To overcome these complexities, automated chromosome karyotype architecture is proposed using the DCNN architecture The proposed method is mainly applied for multiclass classification where the different types of numerical abnormalities are addressed such as Trisomy 13, Trisomy 18, Trisomy 21, Trisomy XXY syndrome, and Monosomy X.

Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations of these diseases. In order to assist the radiologists, computer-aided diagnosis systems have been developed. Besides, the potential of deep convolutional neural networks (CNNs) is also expected to exert on the medical image analysis in recent years. In this paper, we design a new deep convolutional neural network (CNN) architecture to achieve the classification task of ILD patterns. Furthermore, we also propose a novel two-stage transfer learning (TSTL) method to deal with the problem of the lack of training data, which leverages the knowledge learned from sufficient textural source data and auxiliary unlabeled lung CT data to the target domain. We adopt the unsupervised manner to learn the unlabeled data, by which the objective function composed of the prediction confidence and mutual information are optimized. The experimental results show that our proposed CNN architecture achieves desirable performance and outperforms most of the state-of-the-art ones. The comparative analysis demonstrates the promising feasibility and advantages of the proposed two-stage transfer learning strategy as well as the potential of the knowledge learning from lung CT data. Graphical Abstract The framework of the proposed two-stage transfer learning method.

Hyperspectral imagery and machine learning have proven to be powerful, non-invasive, and chemical-free tools for studying tree symbiotic fungi. However, traditional machine learning requires manual feature extraction (feature engineering) of spectral and spatial features of tree symbiotic fungi. Deep convolutional neural networks (CNNs) can extract self and robust features directly from the raw data. In the current study, a deep CNN architecture is proposed to recognize the isolates of dark septate endophytic (DSE) fungal in hyperspectral images. The performance of different CNN approaches (two-dimensional and three-dimensional CNNs) was compared and evaluated based on two independent datasets collected using visible-near-infrared (VNIR) and short-wave-infrared (SWIR) hyperspectral imaging systems. Moreover, the impact of different spectral pre-processing techniques was investigated. The results show that a hybrid CNN architecture (3D-2D CNN), which combines three and two-dimensional CNNs, achieved the best performance for the classification of fungal isolates on SWIR hyperspectral data compared to the same architecture on VNIR hyperspectral data. The best performance is 100% for precision, recall, and overall accuracy. The results also demonstrate that combining different pre-processing techniques on raw SWIR spectra can significantly improve the performance of the CNN models for fungal classification. The hybrid CNN approach with SWIR hyperspectral data provides an efficient method for classifying fungal isolates, which can contribute to the development of accurate and non-destructive tools for evaluating the occurrence of fungal isolates on trees. Such tools can be beneficial for both sustainable agriculture and preserving fungal diversity.

Deep learning–based roadway crack classification with heterogeneous image data fusion

In this paper, we proposed a machine vision system based on deep convolutional neural network (DCNN) architecture for improving the accuracy of classifying three distinct groups of rice kernel images compared with the traditional approaches. The main advantage of the presented method was able to avoid many heuristics as well as manual labor to tune complex parameters according to the domain to reach a modest level of accuracy in the classical feature extraction algorithms. We trained our models using stochastic gradient descent with momentum of 0.9 and weight decay of 0.0005 to optimize the network parameters and minimize the back-propagation error on the training dataset. We used a batch size between 15 and 150 and epochs time configured between 10 and 25. The experiment results showed that the highest accuracy of 99.4% obtained in the training process with batch size of 15 and epoch time of 20. We also compared the DCNN method with the traditional hand-engineered approaches of PHOG-KNN, PHOG-SVM, GIST-KNN, and GIST-SVM for rice kernel classification. The results showed that DCNN routinely outperforms other methods in similar machine vision tasks. The prediction accuracy results for test datasets by PHOG-KNN, PHOG-SVM, GIST-KNN, and GIST-SVM models were 89.1, 76.9, 90.6, and 92.1%, respectively. The highest prediction accuracy of DCNN is 95.5%, which showed the effectiveness of our proposed method for rice kernel classification. The aim of this study is to set up an automatic and accurate intelligent detection system and offering much value to current rice processing industry. With the comparably high classification accuracy, developed neural network could be used as a tool to achieve better and more objective rice quality evaluation at trading points within the rice marketing system.

• Empirical performance assessment of six pre-trained deep CNN architectures (VGG16, VGG19, InceptionV3, DenseNet121, MobileNet, and Xception) for the human face liveness detection application. • Modification of all considered pre-trained DCNN models to make them suitable for human face liveness detection. • Observation of the testing accuracy & HTER observed over a diverse number of epochs for assessing the pre-trained DCNN models. • Fine-tuning of the last two convolution blocks of VGG16 and VGG19 at the constant learning rate to get better test accuracy (100%). Advancement in deep learning is mapping to every field of life and applying it to almost all research problems. Numerous Deep Convolutional Neural Network (DCNN) architectures are being proposed, giving different results based on the depth and value of hyperparameters. The entire development of such DCNN architectures from scratch needs a lot of effort, and such architectures may not be used for other applications than the one they are structured for. Transfer learning is a way to modify these pre-trained networks to make them suitable for newer diverse applications. This paper attempts to empirically assess the performance and suitability of existing pre-trained DCNN architectures for human face liveness detection. Due to the advent of ambient computing for contactless identification of humans using their biometric traits, human face liveness detection proves to be an important research area. Six pre-trained DCNN models, alias VGG16, VGG19, DensNet121, Xception, MobileNet, and InceptionV3, are considered for empirical assessment in human face liveness detection. The method is explored using two face liveness detection datasets - NUAA and Replay-Attack. Face Liveness Accuracy, and Half Total Error Rate (HTER) are considered prime performance evaluation metrics. At a learning rate of 10 −4 , the VGG19 network with scenario “Original VGG” gives the highest face liveness detection accuracy, which is the outcome of current research.

The usage of artificial intelligence (AI) techniques particularly deep learning algorithms, has recently increased dramatically and the healthcare sector is only one of many industries affected Using deep convolutional neural networks (CNNs) enhanced with human auditory inputs we present a novel approach to disease detection in this paper The proposed system aims to provide an effective economical and non invasive method of early disease detection we employ a deep convolutional neural network (CNN) architecture that has been optimized for audio input processing. Further more we use data augmentation methodologies to strengthen the model's capacity to generalize and handle varied patient groups and diseases more effectively. Our all encompassing approach incorporates denoising techniques using Wavelet transform denoising segmentation via K-mean clustering, feature extraction using Recursive Feature Elimination (RFE) and classification using Enhanced Deep Convolutional Neural Networks (EDCNN) which improves the accuracy and dependability of the system in identifying diseases based on human sound signatures.

Agriculture is the most important sector in Indian economy. Plant disease detection is the major challenge in the agriculture sector. An accurate and a faster detection of diseases in plants which substantially reducing economic losses. Manual monitoring of the plant leaf disease is very critical task and also it is time consuming too. So the automated solution is necessary for plant disease identification. Now-a-days deep learning is becoming the standard technique for image classification. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection. Already few architecture has been developed by the researchers for efficient classification plant disease which includes Faster Region-based Convolutional Neural Network(Faster R-CNN), Region-based Fully Convolutional network(R-FCN), and Single Shot Multibox Detector(SSD). Alex Net, Google Net and VGG-16 are the well knows Deep Convolutional Neural Network architecture for general Image classification. But these architecture does not perform well, when multiple diseases affecting the same leaf. To overcome this, A Hybrid Deep Convolutional Neural Network architecture with segmentation is proposed in this research work which comprises of five convolutional layer, five pooling layer and two fully connected layer. Deep convolutional Neural Networks are most commonly used to analyze visual imagery and are frequently working behind the scenes in image classification. In the proposed system of the Faster-RCNN the input images are feed to the CNN, it will increase the accuracy of images to predict the diseases from the plant leaf.

Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer’s disease. Alzheimer’s disease research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person’s mental health. Support vector machine is the first technique. Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several support vector machines kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network architecture to identify Alzheimer’s-related mental disorders. According to the findings, the support vector machines approach accurately recognized over 93% of the photos tested. The deep convolutional neural network approach was one hundred percent accurate during model training, whereas the support vector machines approach achieved just 93 percent accuracy. In contrast to support vector machines accuracy of 89.3%, the deep convolutional neural network model test findings were accurate 98.8% of the time. Based on the findings reported here, the proposed deep convolutional neural network architecture may be used for diagnostic purposes involving the patient’s mental state.

This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy.

Speaking disorders such as stuttering or stammering cause sounds, syllables, or phrases to be repeated or delayed, impairing natural speaking patterns. A person's quality of life may suffer as a result, and communication with others may become challenging. Over 70 million individuals globally experience stuttering. Many people with this illness struggle to fit in and lose confidence while speaking in front of others. Because of this, this research aims to create a thoroughly annotated dataset and, in the end, provide an extensive empirical comparison of deep convolutional neural network (CNN) architectures on the classification of stuttering or Stammering. Short-time fast Fourier transformation (STFT) is used in this study to convert audio data into a visual representation (spectrogram), and Mel Frequency Cepstral Coefficients (MFCC) are then used to extract many audio features to use CNNs to create a workable classification system. Matthew's correlation coefficient (MCC), time to build, and accuracy score are used to evaluate the classifiers' performance. The final findings showed that AlexNet, Le-Net5, ResNet50, LSTM-CNN, and VGG are the top five CNN classifiers that have been investigated. The four designs that are being used have an exceptionally high degree of accuracy, ranging from 90% to 97%.

Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially lower number of parameters. This makes the proposed model suitable for applications such as real-time classification using inexpensive hardware for high-throughput phenotyping pipelines. The GitHub repository at https://github. com/FarhadMaleki/LodgedNet contains code and models.

In this paper research on a compact deep convolutional neural network (DCNN) architecture for age and gender estimation from facial images has been presented. The proposed solution was tested on the FERET and the Adience Benchmark databases. In the first case a 98.6% accuracy for gender and 86.4% for age estimation was obtained. For the Adience database, which contains images recorded in unconstrained conditions and is much more demanding, a 62.0% for gender and 42.0% for age accuracy was obtained. When compared to the reference results on a much larger network, the performance should be considered as satisfactory. The research shows that a compact DCNN with small input images can provide quite good classification results.

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks.

Multispectral imaging devices incorporating up to 256 different spectral channels have recently become available for various healthcare applications, as e.g. laparoscopy, gastroscopy, dermatology or perfusion imaging for wound analysis. Currently, the use of such devices is limited due to very high investment costs and slow capture times. To compensate these shortcomings, single sensors with spectral masking on the pixel level have been proposed. Hence, adequate spectral reconstruction methods are needed. Within this work, two deep convolutional neural networks (DCNN) architectures for spectral image reconstruction from single sensors are compared with each other. Training of the networks is based on a huge collection of different MSI imagestacks, which have been subsampled, simulating 16-channel single sensors with spectral masking. We define a training, validation and test set (‘HITgoC’) resulting in 351 training (631.128 sub-images), 99 validation (163.272 sub-images) and 51 test images. For the application in the field of neurosurgery an additional testing set of 36 image stacks from the Nimbus data collection is used, depicting MSI brain data during open surgery. Two DCNN architectures were compared to bilinear interpolation (BI) and an intensity difference (ID) algorithm. The DCNNs (ResNet-Shinoda) were trained on HITgoC and consist of a preprocessing step using BI or ID and a refinement part using a ResNet structure. Similarity measures used were PSNR, SSIM and MSE between predicted and reference images. We calculated the similarity measures for HitgoC and Nimbus data and determined differences of the mean similarity measure values achieved with the ResNet-ID and baseline algorithms such as BI algorithm and ResNet-Shinoda. The proposed method achieved better results against BI in SSIM (.0644 vs. .0252), PSNR (15.3 dB vs. 9.1 dB) and 1-MSE*100 (.0855 vs. .0273) and compared to ResNet-Shinoda in SSIM (.0103 vs. .0074), PSNR (3.8 dB vs. 3.6 dB) and 1-MSE*100 (.0075 vs. .0047) for HITgoC/Nimbus. In this study, significantly better results for spectral reconstruction in MSI images of open neurosurgery was achieved using a combination of ID-interpolation and ResNet structure compared to standard methods.