Are Vision Transformers Robust to Patch Perturbations?

Modeling accurate text recognizers for scanned Devanagari scripts is a challenging task, primarily due to the ingrained complexities of characters, vowels, conjuncts, and modifiers in the word formation of the script. This paper presents an attention-based convolutional recurrent neural network (CRNN) for improving the text recognition accuracy of the Devanagari script images. The proposed model is an encoder–decoder network with an attention mechanism. The encoder is a convolutional neural network (CNN), which extracts the significant and high-level features from the input images. The encoder also generates the extracted features in an ordered sequence. The decoder uses a bidirectional long short-term (BLSTM) network with an attention mechanism, followed by a connectionist temporal classification (CTC) layer to predict the text in images. The attention mechanism enables the decoder to selectively exploit the high-level features of the encoder congruently. We have trained, validated, and tested the performance of the proposed model on three datasets, viz. publicly available IIIT-H scene image dataset, synthetic dataset, and real dataset curated during this work. We have also compared the proposed model results with work already attempted in the area for the three datasets. We found that the performance of our proposed model outdoes the outcomes obtained from other models on the three datasets.

Image classification is an important task that has a wide range of applications. Convolutional neural networks (CNNs) are a common approach that can achieve promising performance in image classification. However, using CNNs to address a problem requires in-depth knowledge about CNN architectures and how it relates to the problem domain. Genetic programming (GP) as an evolutionary computation method can used to reduce the amount of knowledge required to design a CNN for a given problem domain by automatically searching for the optimal architecture. This paper proposes a new algorithm named, GPCNN, which encodes graph-based CNN architectures as trees and uses genetic operators, i.e. mutation, crossover and selection, to find better architectures. A more flexible crossover, partial subtree crossover, is also proposed to improve the search performance. As an preliminary work, GPCNN did not manage to achieve better performance than the state-of-the-art methods due to the limit on computational resource, but it is able to achieve better results than the baseline methods. More importantly, the proposed tree-based graph representation of CNN allows CNN architecture of various shapes, which has a great potential for future work in evolutionary automatic neural architecture search.

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.

Convolutional neural network (CNN) is a state-of-art deep learning (DL) method with superior performance in image classification. Here, a CNN-based workflow is proposed to discriminate hominid teeth. Our hope is that this method could help confirm otherwise questionable records of Homo from Pleistocene deposits where there is a standing risk of mis-attributing molars of Pongo to Homo. A two-step workflow was designed. The first step is converting the enamel-dentine junction (EDJ) into EDJ card, that is, a two-dimensional image conversion of the three-dimensional EDJ surface. In this step, researchers must carefully orient the teeth according to the cervical plane. The second step is training the CNN learner with labeled EDJ cards. A sample consisting of 53 fossil Pongo and 53 Homo (modern human and Neanderthal) was adopted to generate EDJ cards, which were then separated into training set (n = 84) and validation set (n = 22). To assess the feasibility of this workflow, a Pongo-Homo classifier was trained from the aforementioned EDJ card set, and then the classifier was used to predict the taxonomic affinities of six samples (test set) from von Koenigswald's Chinese Apothecary collection. Results show that EDJ cards in validation set are classified accurately by the CNN learner. More importantly, taxonomic predictions for six specimens in test set match well with the diagnosis results deduced from multiple lines of evidence, implying the great potential of CNN method. This workflow paves a way for future studies using CNN to address taxonomic complexity (e.g., distinguishing Pongo and Homo teeth from the Pleistocene of Asia). Further improvements include visual interpretation and extending the applicability to moderately worn teeth.

Nowadays Convolutional Neural Networks (CNNs) are being employed in a wide range of industrial technologies for a variety of sectors, such as medical, automotive, aviation, agriculture, space, etc. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way. Layer-based details of CNNs along with parameter and floating-point operation number calculations are outlined. Using an evolutionary approach, the majority of the outstanding image classification CNNs, published in the open literature, is introduced with a focus on their accuracy performance, parameter number, model size, and inference speed, highlighting the progressive developments in convolutional operations. Results of a novel investigation of the convolution types and operations commonly used in CNNs are presented, including a timing analysis aimed at assessing their impact on CNN performance. This extensive experimental study provides new insight into the behaviour of each convolution type in terms of training time, inference time, and layer level decomposition. Building blocks for CNN-based object detection are also discussed, such as backbone networks and baseline types, and then representative state-of-the-art designs are outlined. Experimental results from the literature are summarised for each of the reviewed models. This is followed by an overview of recent ADSs related works and current industry activities, aiming to bridge academic research and industry practice on CNNs and ADSs. Design approaches targeted at solving problems of automakers in achieving real-time implementations are also proposed based on a discussion of design constraints, human vs. machine evaluations and trade-off analysis of accuracy vs. size. Current technologies, promising directions, and expectations from the literature on ADSs are introduced including a comprehensive trade-off analysis from a human-machine perspective.

In this paper two new learning-based eXplainable AI (XAI) methods for deep convolutional neural network (DCNN) image classifiers, called L-CAM-Fm and L-CAM-Img, are proposed. Both methods use an attention mechanism that is inserted in the original (frozen) DCNN and is trained to derive class activation maps (CAMs) from the last convolutional layer’s feature maps. During training, CAMs are applied to the feature maps (L-CAM-Fm) or the input image (L-CAM-Img) forcing the attention mechanism to learn the image regions explaining the DCNN’s outcome. Experimental evaluation on ImageNet shows that the proposed methods achieve competitive results while requiring a single forward pass at the inference stage. Moreover, based on the derived explanations a comprehensive qualitative analysis is performed providing valuable insight for understanding the reasons behind classification errors, including possible dataset biases affecting the trained classifier (Source code is made publicly available at: https://github.com/bmezaris/L-CAM).KeywordsExplainable AIXAIImage classificationClass activation mapDeep convolutional neural networksAttentionBias

Convolutional neural networks have demonstrated state-of-the-art performance in image classification and various other computer vision tasks. Plant disease detection is an important area of deep learning which has been addressed by many recent methods. However, there is a dire need to optimize these solutions for resource-constrained portable devices such as smartphones. This is a challenging problem because deep learning models are resource extensive in nature. This paper proposes an efficient method to systematically classify plant disease symptoms using convolutional neural networks. These networks are memory efficient and when coupled with the proposed training configuration it enables rapid development of industrial applications by reducing the training times. Another critical problem arises with the improper distribution of samples among classes known as the class imbalance problem, which is addressed by employing a simple statistical methodology. Transfer learning is a known technique for training small datasets which transfers pre-trained weights learned on a large dataset. However, during transfer learning, negative transfer learning is a common problem. Therefore, a stepwise transfer learning approach is proposed which can help in fast convergence, while reducing overfitting and preventing negative transfer learning during knowledge transfer across domains. The system is trained and evaluated on two plant disease datasets i.e., PlantVillage (a publicly available dataset) and pepper disease dataset provided by the National Institute of Horticultural and Herbal Science, Republic of Korea. The pepper dataset is particularly challenging as it contains images from different parts of the plant such as the leaf, pulp, and stem. The proposed system has dominated the previous works on the PlantVillage dataset and achieved 99% and 99.69% accuracy on the Pepper dataset and PlantVillage datasets, respectively.

This research paper presents an advanced deep learning framework for tomato ripeness and maturity detection, employing Convolutional Neural Networks (CNNs) as the primary tool for automated classification. In particular, the study emphasizes the utilization of the ResNet-152 architecture, a sophisticated deep neural network known for its exceptional performance in image classification tasks. ResNet-152 addresses the challenges of fine-grained categorization by enabling the extraction of intricate visual features such as color nuances, textures, and shapes inherent to tomatoes. This eliminates the necessity for manual feature engineering, enhancing the model's ability to discern between "RIPE," "UNRIPE," and "ROTTEN" tomato classes with unprecedented accuracy. The ResNet-152 architecture's effectiveness lies in its unique design, featuring residual blocks that facilitate the training of very deep networks. This architecture mitigates the vanishing gradient problem and enables the model to learn complex hierarchical features, contributing to its state-of-the-art performance in image classification. In the context of tomato ripeness detection, ResNet-152 acts as a powerful tool for capturing and understanding the intricate visual cues that define different ripeness states, laying the foundation for an efficient and accurate automated sorting system in the agricultural and food processing industries.

Object classification has been one of the main tasks in computer vision. With the fast development of deep learning, its performance in image classification and object recognition has presented dramatic improvements. In this paper, we propose a new deep convolutional neural network (CNN) architecture for robust object classification. The proposed model is fused with three traditional CNN approaches, Densenet201, Resnet50, and our proposed residual CNN. The fused network architecture allows parallel processing of the multiple networks for keeping the system sped up. A single shot deep convolution network is trained as an object detector to generate all possible candidates of different object classes. The output of each neural network is representing a single vote that is used in the classification process. 3-to-1 voting criteria are applied in the final classification decision between the candidate object classes. Several experiments were conducted to evaluate the performance of the proposed network. The experimental results show that the proposed approach has better performance than the networks used in the fusion process when they act individually. It also has lower miss rates when compared to several states of art methodologies.

The purpose of this study is to offer a basic Convolutional Neural Network (CNN) software for classifying epileptic seizures. The recognition of the Electroencephalography (EEG) signal and its distinctive features are required for the diagnosis of epileptic seizures. As a result, it requires a mechanism for recognising and categorising epileptic seizures. As part of a neural network, deep learning is used .CNN has shown excellent performance in image classification and pattern identification. This study utilizes the Hanning window FFT analyser and CNN ConvNet to categorize epileptic seizures. To transform the time-frequency domain of the EEG signal data, this transform produces an image that may be classified into five properties.

Visualizing and analyzing convolution neural networks with gradient information

Due to the high availability of large-scale annotated image datasets, paramount progress has been made in deep convolutional neural networks (CNNs) for image classification tasks. CNNs enable learning highly representative and hierarchical local image features directly from data. However, the availability of annotated data, especially in the medical imaging domain, remains the biggest challenge in the field. Transfer learning can provide a promising and effective solution by transferring knowledge from generic image recognition tasks to the medical image classification. However, due to irregularities in the dataset distribution, transfer learning usually fails to provide a robust solution. Class decomposition facilitates easier to learn class boundaries of a dataset, and consequently can deal with any irregularities in the data distribution. Motivated by this challenging problem, the paper presents Decompose, Transfer, and Compose (DeTraC) approach, a novel CNN architecture based on class decomposition to improve the performance of medical image classification using transfer learning and class decomposition approach. DeTraC enables learning at the subclass level that can be more separable with a prospect to faster convergence.We validated our proposed approach with three different cohorts of chest X-ray images, histological images of human colorectal cancer, and digital mammograms. We compared DeTraC with the state-of-the-art CNN models to demonstrate its high performance in terms of accuracy, sensitivity, and specificity.

The excellent feature extraction ability of deep convolutional neural networks (DCNNs) has been demonstrated in many image processing tasks, by which image classification can achieve high accuracy with only raw input images. However, the specific image features that influence the classification results are not readily determinable and what lies behind the predictions is unclear. This study proposes a method combining the Sobel and Canny operators and an Inception module for ship classification. The Sobel and Canny operators obtain enhanced edge features from the input images. A convolutional layer is replaced with the Inception module, which can automatically select the proper convolution kernel for ship objects in different image regions. The principle is that the high-level features abstracted by the DCNN, and the features obtained by multi-convolution concatenation of the Inception module must ultimately derive from the edge information of the preprocessing input images. This indicates that the classification results are based on the input edge features, which indirectly interpret the classification results to some extent. Experimental results show that the combination of the edge features and the Inception module improves DCNN ship classification performance. The original model with the raw dataset has an average accuracy of 88.72%, while when using enhanced edge features as input, it achieves the best performance of 90.54% among all models. The model that replaces the fifth convolutional layer with the Inception module has the best performance of 89.50%. It performs close to VGG-16 on the raw dataset and is significantly better than other deep neural networks. The results validate the functionality and feasibility of the idea posited.

This paper demonstrates image classification using deep learning. Deep learning has the inherent ability to automatically discover and extract meaningful features for specific applications. Among the popular techniques in deep learning, the convolutional neural network (CNN) stands out. CNN consists of an input layer, hidden layers, and an output layer, where meaningful features are automatically extracted from input images. This paper presents the performance and identifies the most effective CNN architectures for accurately classifying images in the CIFAR-10 dataset. Five CNN architectures were implemented, namely [Architecture 1], [Architecture 2], [Architecture 3], [Architecture 4], and [Architecture 5] using the CIFAR-10 dataset. The architectures were selected based on the need to explore variations in convolutional filter sizes, dense layers, and batch normalization to assess their impact on CIFAR-10 image classification performance. Each architecture was trained on a standard training set and evaluated on a validation set. We used specific details on data preprocessing and training settings for a consistent and fair comparison. After training and evaluation, we have obtained the following results for each architecture. Architecture 1 has a training accuracy of 74.7% and validation accuracy of 76.6%, Architecture 2 has 96.09% and 86.08%, Architecture 3 has 77% and 78.1%, Architecture 4 has 67.91% and 69.54%, and Architecture 5 has 94.64% and 87.34% of training accuracy and validation accuracy respectively. After conducting a comparative analysis, we found that Architecture 5 has achieved the highest validation accuracy in classifying images in the CIFAR-10 dataset. These findings suggest that Architecture 5 is a promising choice for image classification tasks involving the CIFAR-10 dataset.

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).