Attention Based Evolutionary Approach for Image Classification

Deep learning based classification of breast tumors with shear-wave elastography

Detecting objects from images is a challenging problem in the domain of computer vision and plays a very crucial role for wide range of real-time applications. The ever-increasing growth of deep learning due to availability of large training data and powerful GPUs helped computer vision community to build commercial products and services which were not possible a decade ago. Deep learning architectures especially convolutional neural networks have achieved state-of-the-art performance on worldwide competitions for visual recognition like ILSVRC, PASCAL VOC. Deep learning techniques alleviate the need of human expertise from designing the handcrafted features and automatically learn the features. This resulted into use of deep architectures in many domains like computer vision (image classification, visual recognition) and natural language processing (language modeling, speech recognition). Object detection is one such promising area immensely needed to be used in automated applications like self-driving cars, robotics, drone image analysis. This paper analytically reviews state-of-the-art deep learning techniques based on convolutional neural networks for object detection.

Image classification is one of the important problems in the field of machine learning. Deep learning architectures are used in many machine learning applications such as image classification and object detection. The ability to manipulate large image clusters and implement them quickly makes deep learning a popular method in classifying images. This study points out the success of the convolutional neural networks which is the architecture of deep learning, in solving image classification problems. In the study, the convolutional neural network model of the winner of ilsvrc12 competition is implemented. The method distinguishes 1.2 million images with 1000 categories in success. The application is performed with the caffe library, and the image classification process is employed. In the application that uses the speed facility provided by GPU, the test operation is performed by using the images in Caltech-101 dataset.

Deep learning-based image classification for online multi-coal and multi-class sorting

This paper explores the application of deep learning architectures in retinal image analysis, with a particular focus on the early detection of diabetic retinopathy (DR). Leveraging large-scale retinal imaging datasets, the study examines the diagnostic capacity of advanced neural models in identifying subtle pathological markers such as microaneurysms, hemorrhages, and exudates at the earliest clinical stages. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid deep learning frameworks are evaluated for their performance in image classification and lesion localization. Results demonstrate that CNN-based architectures achieve an accuracy of 94.2%, with sensitivity and specificity values exceeding 93%, underscoring their robustness in medical image interpretation. Hybrid models integrating CNN and attention mechanisms further enhance feature discrimination, achieving 95.1% accuracy and improved area under the curve (AUC) metrics. RNN-driven architectures exhibit competitive performance, particularly in sequential image feature learning, with overall accuracy surpassing 92%. The findings highlight the transformative role of deep learning in automating ophthalmic diagnostics, enabling scalable, precise, and timely detection of diabetic retinopathy to mitigate preventable vision loss.

Steering angle prediction plays a crucial role in the control of Autonomous Vehicles (AVs) and has garnered the interest of researchers, manufacturers, and insurance companies within the automotive industry. Various Deep Learning (DL) architectures have been utilized to forecast the steering angle of AVs across different scenarios. An examination focusing on steering angle prediction through deep learning algorithms can assist seasoned researchers in pinpointing areas that necessitate further advancement. Additionally, newcomers in the field can utilize this study as a foundation. This paper offers an extensive analysis of the recent progress achieved in DL architectures concerning the steering angle prediction of AVs. A comprehensive taxonomy outlining the application of DL in steering angle prediction of AVs has been developed. The survey provides a succinct synthesis, summary, and analysis of research findings. It is evident that Convolutional Neural Network (CNN) is the preferred choice among researchers for predicting the steering angle of autonomous vehicles, compared to other DL architectures. Furthermore, existing research challenges have been identified. The primary obstacle encountered in DL-based steering angle prediction of AVs is the scarcity of real-world datasets, leading researchers to heavily rely on data generated from simulated environments. Lastly, potential alternative approaches to address the identified research challenges have been suggested, indicating promising avenues for future research endeavours.

This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β, using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β. This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.

The exponential growth in the number of complex datasets every year requires\nmore enhancement in machine learning methods to provide robust and accurate\ndata classification. Lately, deep learning approaches have achieved surpassing\nresults in comparison to previous machine learning algorithms. However, finding\nthe suitable structure for these models has been a challenge for researchers.\nThis paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble,\ndeep learning approach for classification. RMDL solves the problem of finding\nthe best deep learning structure and architecture while simultaneously\nimproving robustness and accuracy through ensembles of deep learning\narchitectures. In short, RMDL trains multiple randomly generated models of Deep\nNeural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural\nNetwork (RNN) in parallel and combines their results to produce better result\nof any of those models individually. In this paper, we describe RMDL model and\ncompare the results for image and text classification as well as face\nrecognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for\nimage classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text\nclassification. Lastly, we used ORL dataset to compare the model performance on\nface recognition task.\n

The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution. Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF). Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value. MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF.

Natural language processing (NLP) techniques have evolved substantially in recent years, with transformer-based architectures emerging as powerful tools for textual analysis across various fields. This paper investigates the underexplored area of Arabic legal text classification through the implementation of transformer-based language models integrated with deep learning (DL) architectures. Even though transformer models are widely used in legal NLP for high-resource languages, their application to Arabic legal texts remains limited due to linguistic complexities and dataset scarcity. We introduce a classification model for Arabic legal text and present a new dataset of over 27,000 Moroccan legal cases with seven categories. Our methodology implements a segment-based approach to overcome the token limitations of transformer models, processing document segments individually before aggregating embeddings to create complete document representations. Four deep learning architectures are then used to classify those documents. Experimental evaluations demonstrate that MARBERTv2 combined with Gated Recurrent Unit (GRU) architecture achieves superior performance with 95.01% accuracy and Matthews Correlation Coefficient (MCC) of 0.9090.

Remote-sensing images comprise massive amount of spatial and semantic data that can be employed for several applications. Presently, deep learning (DL) models for RS image processing become a familiar research area. Due to the advancements of recent satellite imaging sensors , the issue of huge amount of data processing becomes a challenging problem. To accomplish this, deep transfer learning (DTL) models are developed to resolve the semantic gap among various datasets This study develops a new DTL-based fusion model for environmental remote-sensing image classification, called DTLF-ERSIC technique. The proposed technique focuses on the design of fusion model to combine multiple feature vectors and thereby attains maximum classification performance. The DTLF-ERSIC technique incorporates the entropy-based fusion of three feature extraction techniques, namely, Discrete Local Binary Pattern (DLBP), Residual Network (ResNet50), and EfficientNet models. Besides, a rain optimization algorithm (ROA) with fuzzy rule-based classifier (FRC) is applied to predict the class labels of the test RS images and shows the novelty of the work. A comprehensive experimental analysis of the DTLF-ERSIC technique takes place on benchmark dataset and examined the results in terms of different performance measures. The simulation results reported the supremacy of the DTLF-ERSIC technique over the recent state-of-art techniques.

In the last few years, Deep Learning (DL) has been showing superior performance in different modalities of bio-medical image analysis. Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology. In this paper, we propose a new DL architecture, the NABLA-N network (∇N-Net), with better feature fusion techniques in decoding units for dermoscopic image segmentation tasks. The ∇N-Net has several advances for segmentation tasks. First, this model ensures better feature representation for semantic segmentation with a combination of low to high-level feature maps. Second, this network shows better quantitative and qualitative results with the same or fewer network parameters compared to other methods. In addition, the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model is used for skin cancer classification. The proposed ∇N-Net network and IRRCNN models are evaluated for skin cancer segmentation and classification on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC-2018). The experimental results show superior performance on segmentation tasks compared to the Recurrent Residual U-Net (R2U-Net). The classification model shows around 87% testing accuracy for dermoscopic skin cancer classification on ISIC2018 dataset.

Deep learning algorithms have the potential to automate the examination of medical images obtained in clinical practice. Using digitized medical images, convolution neural networks (CNNs) have demonstrated their ability and promise to discriminate among different image classes. As an initial step towards explainability in clinical diagnosis, deep learning models must be exceedingly precise, offering a measure of uncertainty for their predictions. Such uncertainty-aware models can help medical professionals in detecting complicated and corrupted samples for re-annotation or exclusion. This paper proposes a new model and data-agnostic mechanism, called Actionable Uncertainty Quantification Optimization (AUQantO) to improve the performance of deep learning architectures for medical image classification. This is achieved by optimizing the hyperparameters of the proposed entropy-based and Monte Carlo (MC) dropout uncertainty quantification techniques escorted by single- and multi-objective optimization methods, abstaining from the classification of images with a high level of uncertainty. This helps in improving the overall accuracy and reliability of deep learning models. To support the above claim, AUQantO has been validated with four deep learning architectures on four medical image datasets and using various performance metric measures such as precision, recall, Area Under the Receiver Operating Characteristic (ROC) Curve score (AUC), and accuracy. The study demonstrated notable enhancements in deep learning performance, with average accuracy improvements of 1.76% and 2.02% for breast cancer histology and 5.67% and 4.24% for skin cancer datasets, utilizing two uncertainty quantification techniques, and AUQantO further improved accuracy by 1.41% and 1.31% for brain tumor and 4.73% and 1.83% for chest cancer datasets while allowing exclusion of images based on confidence levels.

Detection of smoke from videos captured by surveillance cameras in outdoor environments is one of the useful outcome of Internet of Things (IoT) applications. The potential benefit increases when deep learning (DL) architectures are involved. However, an inherent difficulty is to detect smoke while natural events like fog exists. The effectiveness of color spaces in detection performance has not yet fully evaluated in those architectures. Moreover, the energy and memory requirements of DL architectures may not be applicable for handling IoT implementation demands. Therefore, in this work, a DL architecture with a suitable color space model, applicable for IoT implementations is proposed to detect smoke from videos in foggy environment. By collecting several videos including smoke samples, the performance comparison of popular and the state-of-the-art DL architectures denoted the outperforming result according to both accuracy and memory usage.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.