A common convolutional neural network model to classify plain, rolled and latent fingerprints

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

Comparative analysis of deep convolution neural network models on small scale datasets

Emotions expressed by humans can be identified from facial expressions, speech signals, or physiological signals. Among them, the use of physiological signals for emotion classification is a notable emerging area of research. In emotion recognition, a person’s electrocardiogram (ECG) and galvanic skin response (GSR) signals cannot be manipulated, unlike facial and voice signals. Moreover, wearables such as smartwatches and wristbands enable the detection of emotions in people’s naturalistic environment. During the COVID-19 pandemic, it was necessary to detect people’s emotions in order to ensure that appropriate actions were taken according to the prevailing situation and achieve societal balance. Experimentally, the duration of the emotion stimulus period and the social and non-social contexts of participants influence the emotion classification process. Hence, classification of emotions when participants are exposed to the elicitation process for a longer duration and taking into consideration the social context needs to be explored. This work explores the classification of emotions using five pretrained convolutional neural network (CNN) models: MobileNet, NASNetMobile, DenseNet 201, InceptionResnetV2, and EfficientNetB7. The continuous wavelet transform (CWT) coefficients were detected from ECG and GSR recordings from the AMIGOS database with suitable filtering. Scalograms of the sum of frequency coefficients versus time were obtained and converted into images. Emotions were classified using the pre-trained CNN models. The valence and arousal emotion classification accuracy obtained using ECG and GSR data were, respectively, 91.27% and 91.45% using the InceptionResnetV2 CNN classifier and 99.19% and 98.39% using the MobileNet CNN classifier. Other studies have not explored the use of scalograms to represent ECG and GSR CWT features for emotion classification using deep learning models. Additionally, this study provides a novel classification of emotions built on individual and group settings using ECG data. When the participants watched long-duration emotion elicitation videos individually and in groups, the accuracy was around 99.8%. MobileNet had the highest accuracy and shortest execution time. These subject-independent classification methods enable emotion classification independent of varying human behavior.

Land-cover and land-use classification from aerial images is a challenging problem due to high intra-class diversity and inter-class similarities of the images. To analyze the performances of deep convolutional neural network (CNN) models in this domain, we provide three pre-trained CNN models that are adapted to NWPU-RESISC45 dataset using three different training splits, i.e., 80%, 20%, and 10% ratios. The architectures of all three models are redesigned to be modest in size and their structure is kept simple, yet when tested with the NWPU-RESISC45 dataset, all three models perform comparably to the state-of-the-art models. Each of these models is then used to classify the scenes taken from five well-known datasets in this domain without any fine-tuning. We aim to assess the generalization capabilities of these models on the selected datasets. For better analysis, we considered using top-3 and top-5 accuracies of the models in addition to the best predicted category (top-1) that is usually reported by the models. This way of interpretation is very suitable in this domain, since the datasets contain a high number of fine grained categories with large semantic overlaps. We empirically show that the proposed CNN models actually learn the relevant semantic features in the aerial images better than we observed via standard measures. To the best of our knowledge, this is the first work in this domain that analyzes and presents model generalization performances the way we presented it here.

Visual attention mechanism has been widely used in computer vision and plays a vital role in the research of human action recognition. In this paper, we explore a novel moving target detection mechanism for human action recognition and propose a new 3D CNN (3D Convolutional Neural Network) model, dubbed LaM-2SRN (Local Features Enhanced and Moving target detected 2Stream-ResNet) for extracting and learning attention-enhanced spatiotemporal features. The contributions of this paper are as follow: First, the traditional CAM (Class Activation Map) based visual attention algorithm is used to obtain the optical flow information of the human region, thus eliminating the influence of irrelevant optical flow information (such as background clutter). Second, the ViBe algorithm is used to identify the moving target in the continuous frame, retain the optical flow information of the moving target, and make it complement with the optical flow information of the human region, to obtain a complete motion descriptor. After the motion information is marked in the video frame, we put the marked video frames and the original video frames into the 3D CNN and 2D CNN models respectively, to acquired mixed descriptor, which serves as the basis for video classification. Different from most of the previous target detection algorithms, this paper not only detects the human target, but also detects the moving foreground targets in the video, so that the pre-trained CNN model can obtain more complete motion information from videos. We use RGB-only video data for evaluation on two benchmarks: UCF101 and HMDB51, the experimental results demonstrate that our LaM-2SRN is comparable to the previous state-of-the-art algorithms.

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework

As an important part of emotion research, facial expression recognition is a necessary condition for intelligent interaction between human and machine, which has an important research significance and a potential commercial value. Convolutional neural network (CNN) is an effective method to recognize facial emotions, which can perform feature extraction and classification simultaneously, and can automatically discover multiple levels of representations in data. Due to the fact that there are millions of parameters involved in training the convolutional neural network model, and there is a large demand for marked samples, transfer learning is often used to fine-tune the pre-trained model for a small target sample set. However, there are often some content differences between data sets during deep transfer learning, which will affect the recognition ability of feature extraction. In order to improve the facial expression recognition ability in transfer learning, a hybrid transfer learning model based on an improved convolution restricted boltzmann machine (CRBM) model and a CNN model is proposed in this paper. This method is fused by two learning abilities of these two models. When the pre-trained CNN model is transferred to a small target set, the CRBM is used to replace the full connection layer in the CNN model, and the CRBM layer and the sofmax layer will be retrained on the target set. The added CRBM layer can not only fully connects all feature maps, but can also learn about the unique statistical characteristics about the target set, which eliminates the influence of content differences between data sets, and extracts higher-order statistical features of facial expression images from the target set. The proposed method is evaluated based on four publicly available facial expression databases: JAFFE, FER2013, SFEW and RAF-DB. The new method can achieve better performance than most state-of-the-art methods, and it can effectively prevent the negative influence of transfer learning features between different data sets.

Rooftop farming in urban places is gaining more popularity which increases the cultivation of organic vegetables on the rooftop of houses and buildings with the minimal utilization of water. But rooftop farming is more vulnerable to pest infestation which reduces the quality of plants. Urban residents are novices in farming, and they are unaware of the pest attacks. Various researchers have proposed pest identification systems using image processing techniques and machine learning algorithms specific to particular disease which shows less accuracy on generaliztion and not user-friendly. To provide user-friendly pest identification system, this paper proposes a mobile based pest identification system using the concept of pre-trained convolutional neural network model – AlexNet. Experimental results have been analyzed with various rooftop pests using different kernel sizes and layers of convolutional neural network. In addition, the best evaluated pre-trained model has been converted to a mobile application using REST API for the recommendation of pesticide to the novice user.

Convolutional neural networks (CNNs) are an effective technique for hyperspectral image classification. Deep learning models require a large number of labeled and diverse samples to properly train a CNN model. But a training set is often not large enough. Transfer learning can help to overcome the need for training sets. In this paper, six pre-trained CNN models: EfficientNetB0, EfficientNetB7, ResNet50, VGG19, DenseNet121 and DenseNet201 are fine-tuned for hyperspectral image classification. The experiments are carried out on two benchmark images Houston and Kennedy Space Center (KSC). The results show that hyperspectral images can be classified with good accuracy by fine-tuned pre-trained CNN models. As compared to training a model from scratch, fine-tuning takes a small number of epochs. Thus, alleviating the requirement for high-end computing resources. Among the tested models, VGG19 achieves the best accuracy of 95.77% for the KSC image, while EfficientNetB0 performs better than others with 90.79% accuracy for the Houston image.

Deep neural networks such as Convolutional Neural Networks (CNNs) have achieved several significant milestones in visual data analytics. Benefited from transfer learning, many researchers use pre-trained CNN models to accelerate the training process. However, there is still uncertainty about the deep learning models, structures, and applications. For instance, the diversity of the datasets may affect the performance of each pre-trained model. Therefore, in this paper, we proposed a new approach based on genetic algorithms to select or regenerate the best pre-trained CNN models for different visual datasets. A new genetic encoding model is presented which denotes different pre-trained models in our population. During the evolutionary process, the optimal genetic code that represents the best model is selected, or new competitive individuals are generated using the genetic operations. The experimental results illustrate the effectiveness of the proposed framework which outperforms several existing approaches in visual data classification.

Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks

Scene classification of high resolution remote sensing images plays an important role for a wide range of applications. While significant efforts have been made in developing various methods for scene classification, most of them are based on handcrafted or shallow learning-based features. In this paper, we investigate the use of deep convolutional neural network (CNN) for scene classification. To this end, we first adopt two simple and effective strategies to extract CNN features: (1) using pre-trained CNN models as universal feature extractors, and (2) domain-specifically fine-tuning pre-trained CNN models on our scene classification dataset. Then, scene classification is carried out by using simple classifiers such as linear support vector machine (SVM). In our work, three off-the-shelf CNN models including AlexNet [1], VGGNet [2], and GoogleNet [3] are investigated. Comprehensive evaluations on a publicly available 21 classes land use dataset and comparisons with several state-of-the-art approaches demonstrate that deep CNN features are effective for scene classification of high resolution remote sensing images.

Diabetic retinopathy (DR) is a common eye disease that people get from diabetes. About 33.7% of the people with diabetes have DR. With our datas, which are pictures of the eyeball with and without DR, we tried different convolutional neural network (CNN) models to get the best accuracy score. We tested our datas with a default CNN model, and 5 different pre-trained models: MobileNet, VGG16, VGG19, Inception V3, and Inception ResNet V2. The default CNN model didn’t perform very well, getting only 10.4%. The pre-trained model also didn’t perform as good as expected, so we decided to use GRU with the models, which increases the score. For the higher accuracy, we added bidirectional GRU to train the whole parameters in the model. The 5 different pre-trained models scored an average of 74.2% accuracy score, and Inception ResNet V2 with bidirectional GRU included scored the highest accuracy, achieving 83.57%. For additional study, we used a class activation map to spot the abnormal parts of the eyeball with DR, and we could spot abnormal veins and bleeding on the eyeball. However, our research has limitations on that we did not use the segmentation methods, which is more advanced technique compared to classification, such as U-net, Fully Convolutional Network (FCN), Deep Lab V3, and Feature Pyramid Network. Furthermore, even though our model classified 5 different classes, the fact that the highest accuracy score was lower than 90% is also a limitation. For further study, we would prepare a masking method for applying segmentation methods to our dataset.

Rice is the staple food in Bangladesh. Every year many crops are lost because of the disease of the rice plant. Our farmers are facing a great loss every year as well as rice productivity is declining. Some diseased and healthy leaf images of rice plants have been collected. The images have been classified into four classes. The classes are Brown Spot, Hispa, Leaf Blast diseased leaves, and Healthy leaves, usually known as Oryza Saiva diseases of rice. Deep learning is very common and popular to analyze and make predictions from these kind of image data. In this research, the CNN (Convolutional Neural Network) models have been used to predict the classes. Among various CNN models, VGG16, MobileNet, ResNet50, and DenseNet121 pre-trained models have been applied. Among those pre-trained models, the highest accuracy 95.3% has been found from the VGG16 model. In addition, an Android mobile app has been developed using the highest performant VGG16 trained model. Using this mobile app, farmers will be able to upload a photo and predict the disease of that rice plant or not in a convenient way.

Disease is a highly stressed topic in current society, especially the importance of early diagnosis and disease prevention with the help of new technology. Doctors can diagnose diseases like lung cancers, but the process can be more time-consuming, expensive, and inaccurate compared to relying on technology alone. Although previous applications of artificial intelligence exist in the clinical world, our goal was to find which deep learning model would be most accurate in predicting lung cancer. We hypothesized that autoencoders would perform better than other deep learning models such as convolutional neural networks (CNN) and pre-trained models based on the strength and reliability of the data created by the encoder for classification. We studied Kaggle dataset “Chest CT-Scan Images Dataset,” which contains images of both benign and malignant tumors, with malignant categories of lung squamous cell carcinoma, large cell carcinoma, and adenocarcinoma. We trained and tested each model with this dataset on the machine learning tool Google Colaboratory. Based on the results, the pre-trained CNN model had the accuracy at correctly detecting lung cancers, while the autoencoder had the lowest compared to the others. Overall, our results do not support our original hypothesis and instead show that pre-trained models may be more applicable in lung cancer diagnosis.