Feasibility of Automated Black Ice Segmentation in Various Climate Conditions Using Deep Learning

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

Fingerprint classification helps in reducing the number of comparisons during the matching stage in automatic fingerprint identification system. In this study, a convolutional neural network model is proposed for classification of plain, rolled and latent fingerprints. We first propose a new convolutional neural network model initialised with random weights and train the model on fingerprint images. Then we fine-tune two pre-trained convolutional neural network models on fingerprint images. Finally, we compare these three models: two pre-trained models and a custom convolutional neural network model initialised with random weights. We show that pre-trained models can achieve the state-of-the-art results on other similar tasks with no or little fine-tuning. We also show that training data size and depth of the network have a serious impact on the overall performance of deep networks. Dropout is used to enhance the performance of deep networks where the labelled training data is not of sufficient size. All the three models trained on NIST DB4 fingerprint and IIIT-D latent fingerprint databases report good accuracy. By only fine-tuning the pre-trained convolutional neural network model, we get the accuracy of 99%, easily out-performing the state-of-the-art.

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

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.

Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. While many deep learning applications have been used to identify diseased plants and aims to increase the detection rate, the limitation of the large parameter size in the models persist. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.

Coffee bean production can encounter challenges due to fluctuations in global coffee prices, impacting the economic stability of some countries that heavily depend on coffee production. The primary objective is to evaluate how effectively various pre-trained models can predict coffee types using advanced deep learning techniques. The selection of an optimal pre-trained model is crucial, given the growing popularity of specialty coffee and the necessity for precise classification. We conducted a comprehensive comparison of several pre-trained models, including AlexNet, LeNet, HRNet, Google Net, Mobile V2 Net, ResNet (50), VGG, Efficient, Darknet, and DenseNet, utilizing a coffee-type dataset. By leveraging transfer learning and fine-tuning, we assess the generalization capabilities of the models for the coffee classification task. Our findings emphasize the substantial impact of the pre-trained model choice on the model's performance, with certain models demonstrating higher accuracy and faster convergence than conventional alternatives. This study offers a thorough evaluation of pre-trained architectural models regarding their effectiveness in coffee classification. Through the evaluation of result metrics, including sensitivity (1.0000), specificity (0.9917), precision (0.9924), negative predictive value (1.0000), accuracy (1.0000), and F1 score (0.9962), our analysis provides nuanced insights into the intricate landscape of pre-trained models.

This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation for effective treatment and disease monitoring. Multimodal image fusion has the potential to offer more comprehensive information and more precise segmentation, and DL techniques have achieved remarkable progress in this domain. This paper starts with an introduction to liver cancer, then explains the preprocessing and fusion methods for multimodal images, then explores the application of DL methods in this area. Various DL architectures such as convolutional neural networks (CNN) and U-Net are discussed and their benefits in multimodal image fusion segmentation. Furthermore, various evaluation metrics and datasets currently used to measure the performance of segmentation models are reviewed. While reviewing the progress, the challenges of current research, such as data imbalance, model generalization, and model interpretability, are emphasized and future research directions are suggested. The application of DL in multimodal image segmentation for liver cancer is transforming the field of medical imaging and is expected to further enhance the accuracy and efficiency of clinical decision making. This review provides useful insights and guidance for medical practitioners.

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.

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.

The International Classification of Diseases (ICD) serves as a widely employed framework for assigning diagnosis codes to electronic health records of patients. These codes facilitate the encapsulation of diagnoses and procedures conducted during a patient’s hospitalisation. This study aims to devise a predictive model for ICD codes based on the MIMIC-III clinical text dataset. Leveraging natural language processing techniques and deep learning architectures, we constructed a pipeline to distill pertinent information from the MIMIC-III dataset: the Medical Information Mart for Intensive Care III (MIMIC-III), a sizable, de-identified, and publicly accessible repository of medical records. Our method entails predicting diagnosis codes from unstructured data, such as discharge summaries and notes encompassing symptoms. We used state-of-the-art deep learning algorithms, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, bidirectional LSTM (BiLSTM) and BERT models after tokenizing the clinical test with Bio-ClinicalBERT, a pre-trained model from Hugging Face. To evaluate the efficacy of our approach, we conducted experiments utilizing the discharge dataset within MIMIC-III. Employing the BERT model, our methodology exhibited commendable accuracy in predicting the top 10 and top 50 diagnosis codes within the MIMIC-III dataset, achieving average accuracies of 88% and 80%, respectively. In comparison to recent studies by Biseda and Kerang, as well as Gangavarapu, which reported F1 scores of 0.72 in predicting the top 10 ICD-10 codes, our model demonstrated better performance, with an F1 score of 0.87. Similarly, in predicting the top 50 ICD-10 codes, previous research achieved an F1 score of 0.75, whereas our method attained an F1 score of 0.81. These results underscore the better performance of deep learning models over conventional machine learning approaches in this domain, thus validating our findings. The ability to predict diagnoses early from clinical notes holds promise in assisting doctors or physicians in determining effective treatments, thereby reshaping the conventional paradigm of diagnosis-then-treatment care. Our code is available online.

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

The Breast Cancer is second of the dangerous type diseases that is very effective for women in the world. Breast Cancer is spread very fast in last stage. It’s not give a more time for patients for treatment. We need accurate and efficient diagnosis system over a short period of time that can processed a large amount of data in a short time. It’s only possible using deep learning techniques. We are studying on a new diagnosis system for detecting Breast cancer in early stage. But in this paper we are describing the all techniques and images processing method for segmentation and filter images for breast cancer diagnosis. In this paper we have describe all algorithm and techniques for image pre-processing and post progressing. We have reviewed more than 20 papers from different publisher and find out that many researcher working on breast cancer diagnosis techniques. It’s a very latest and effective techniques for helping and saving the human life. In this paper we have also describe some experimental part of images segmentation and filter images with different filter method. It’s a very interesting area for researcher. In this paper we have used (Mammography Image Analysis Society) MIAS dataset having 95 images in .pgm format with 1024 X 1024 pixel. All paper divided in two main part first is data collection and image pre-processing. In this paper we have describe some most used deep learning pre-trained model that is very effective for images pre-processing. As per reviewed study the AlexNet, GoogleNet and VGG16 are provided best accuracy for detecting breast cancer in mammography images. We also used many filter method for image quality improvement. Filters are used to filter unwanted things or object in a spatial domain or surface. The main objective of the filter are to improve the quality of the images by enhancing and improve interoperability of the images. In this paper we also showing the comparison between most used pre-trained model that are used for breast cancer diagnosis.

Chronic Venous Insufficiency (CVI) is a venous incompetence condition that leads to improper blood circulation from the lower limbs towards the heart. This occurs as a result of blood pooling in the veins of the leg, resulting in twisted, dilated, and tortuous veins. Aging, obesity, prolonged standing or sitting, and lack of mobility are all important causes of the occurrence of this chronic disease. The cost of CVI diagnosis and treatment is extremely high. Infrared thermographic image analysis is used for early detection and reduces the cost of diagnosis. Deep learning (DL) techniques play an important role in early prediction and may aid clinicians in diagnosing CVI. An automated classification model will assist the physician in making a precise diagnosis of the abnormal vein and treating the patient according to the severity of the condition. There is a need for a model that can perform successful classification without the need for pre-processing when compared to the traditional machine learning (ML) methods that depend on ideal manual feature extraction to achieve optimal outcomes. In this research, we recommend the customized DenseNet-121 architecture for CVI detection and compare it with other advanced DL models to determine its efficacy. DenseNet-121 and other pre-trained convolutional neural network models, including EfficientNetB0 and Inception_v3, were trained using a transfer learning strategy. The experimental findings indicate that the proposed modified DenseNet-121 model outperformed other classical methods. The reported results provide evidence of the robustness of the suggested method in addition to the high accuracy that it possessed, as shown by the overall testing accuracy of 97.4%. Thus, this study can be considered as a non-invasive and cost-effective approach for diagnosing chronic venous insufficiency condition in lower extremity.

Skin conditions are becoming increasingly prevalent, and many of these ailments carry concealed risks that can elevate the likelihood of developing skin cancer. Given the low quality of images associated with such conditions, diagnosing these diseases traditionally required advanced medical expertise and specialized equipment. Furthermore, manually detecting skin disorders is often subjective, time-consuming, and demands considerable human effort. This highlights the need for an automated, computer-assisted system capable of diagnosing skin conditions without human intervention. To achieve this, Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), will be employed to classify skin diseases using dermoscopic images. The dataset for this study comes from the MNIST HAM10000 collection, which contains 10,015 images and was made available by the International Skin Imaging Collaboration (ISIC). The data is divided into seven categories, including skin cancer. For image classification, a pre-trained CNN model will be utilized. One key challenge is the imbalanced nature of the dataset. To mitigate this, data augmentation techniques were applied, helping to reduce class imbalance and ensure that classification accuracy across categories was not dominated by the majority class.