Search Results (52)

Background/Objectives: Melanoma is an aggressive type of skin cancer that poses serious health risks if not detected in its early stages. Although early diagnosis enables effective treatment, delays can result in life-threatening consequences. Traditional diagnostic processes predominantly rely on the subjective expertise of dermatologists, which can lead to variability and time inefficiencies. Consequently, there is an increasing demand for automated systems that can accurately classify melanoma lesions and retrieve visually similar cases to support clinical decision-making. Methods: This study proposes a transfer learning (TL)-based deep learning (DL) framework for the classification of melanoma images and the enhancement of content-based image retrieval (CBIR) systems. Pre-trained models including DenseNet121, InceptionV3, Vision Transformer (ViT), and Xception were employed to extract deep feature representations. These features were integrated using a weighted fusion strategy and classified through an Ensemble learning approach designed to capitalize on the complementary strengths of the individual models. The performance of the proposed system was evaluated using classification accuracy and mean Average Precision (mAP) metrics. Results: Experimental evaluations demonstrated that the proposed Ensemble model significantly outperformed each standalone model in both classification and retrieval tasks. The Ensemble approach achieved a classification accuracy of 95.25%. In the CBIR task, the system attained a mean Average Precision (mAP) score of 0.9538, indicating high retrieval effectiveness. The performance gains were attributed to the synergistic integration of features from diverse model architectures through the ensemble and fusion strategies. Conclusions: The findings underscore the effectiveness of TL-based DL models in automating melanoma image classification and enhancing CBIR systems. The integration of deep features from multiple pre-trained models using an Ensemble approach not only improved accuracy but also demonstrated robustness in feature generalization. This approach holds promise for integration into clinical workflows, offering improved diagnostic accuracy and efficiency in the early detection of melanoma. Full article

The progress of artificial intelligence (AI), particularly its core algorithms—machine learning (ML) and deep learning (DL)—has been significant in the medical field, impacting both scientific research and clinical practice. These algorithms are now capable of analyzing ultrasound images, processing them, and providing outcomes, such as determining the benignity or malignancy of thyroid nodules. This integration into ultrasound machines is referred to as computer-aided diagnosis (CAD). The use of such software extends beyond ultrasound to include cytopathological and molecular assessments, enhancing the estimation of malignancy risk. AI’s considerable potential in cancer diagnosis and prevention is evident. This article provides an overview of AI models based on ML and DL algorithms used in thyroid diagnostics. Recent studies demonstrate their effectiveness and diagnostic role in ultrasound, pathology, and molecular fields. Notable advancements include content-based image retrieval (CBIR), enhanced saliency CBIR (SE-CBIR), Restore-Generative Adversarial Networks (GANs), and Vision Transformers (ViTs). These new algorithms show remarkable results, indicating their potential as diagnostic and prognostic tools for thyroid pathology. The future trend points to these AI systems becoming the preferred choice for thyroid diagnostics. Full article

This paper addresses the challenges of intent recognition in collaborative Autonomous Underwater Vehicle (AUV) search missions, where multiple AUVs must coordinate effectively despite environmental uncertainties and communication limitations. We propose a consensus-based intent recognition (CBIR) method grounded in the Belief–Desire–Intention (BDI) framework. The CBIR approach incorporates fuzzy inference and deep learning techniques to predict AUV intentions with minimal data exchange, improving the robustness and efficiency of collaborative decision making. The system uses a behavior modeling phase to map state features to actions and a deep learning-based intent inference phase, leveraging a residual convolutional neural network (ResCNN) for accurate intent prediction. The experimental results demonstrate that the proposed ResCNN network improves intent recognition accuracy, enhances the efficiency of collaborative search missions, and increases the success rate. Full article

Background/Objectives: Early detection and diagnosis are important when treating gallbladder (GB) diseases. Poorer clinical outcomes and increased patient symptoms may result from any error or delay in diagnosis. Many signs and symptoms, especially those related to GB diseases with similar symptoms, may be unclear. Therefore, highly qualified medical professionals should interpret and understand ultrasound images. Considering that diagnosis via ultrasound imaging can be time- and labor-consuming, it may be challenging to finance and benefit from this service in remote locations. Methods: Today, artificial intelligence (AI) techniques ranging from machine learning (ML) to deep learning (DL), especially in large datasets, can help analysts using Content-Based Image Retrieval (CBIR) systems with the early diagnosis, treatment, and recognition of diseases, and then provide effective methods for a medical diagnosis. Results: The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. The cosine method was preferred as the similarity measurement metric. Conclusions: Our proposed CBIR model achieved successful results from six other different models. The AP value obtained in the proposed model is 0.94. This value shows that our CBIR-based model can be used to detect GB diseases. Full article

Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, an essential tool to support pathologists. Deep learning models like Convolutional Neural Networks and Vision Transformers improve the extraction of these feature descriptors. These models typically generate embeddings by leveraging deeper single-scale linear layers or advanced pooling layers. However, these embeddings, by focusing on local spatial details at a single scale, miss out on the richer spatial context from earlier layers. This gap suggests the development of methods that incorporate multi-scale information to enhance the depth and utility of feature descriptors in histopathological image analysis. In this work, we propose the Local–Global Feature Fusion Embedding Model. This proposal is composed of three elements: (1) a pre-trained backbone for feature extraction from multi-scales, (2) a neck branch for local–global feature fusion, and (3) a Generalized Mean (GeM)-based pooling head for feature descriptors. Based on our experiments, the model’s neck and head were trained on ImageNet-1k and PanNuke datasets employing the Sub-center ArcFace loss and compared with the state-of-the-art Kimia Path24C dataset for histopathological image retrieval, achieving a Recall@1 of 99.40% for test patches. Full article

Background: The morphological type of the acromion may play a role in the etiopathogenesis of various pathologies, such as shoulder impingement syndrome and rotator cuff disorders. Therefore, it is important to determine the acromion’s morphological types accurately and quickly. In this study, it was aimed to detect the acromion shape, which is one of the etiological causes of chronic shoulder disorders that may cause a decrease in work capacity and quality of life, on shoulder MR images by developing a new model for image retrieval in Content-Based Image Retrieval (CBIR) systems. Methods: Image retrieval was performed in CBIR systems using Convolutional Neural Network (CNN) architectures and textural-based methods as the basis. Feature maps of the images were extracted to measure image similarities in the developed CBIR system. For feature map extraction, feature extraction was performed with Histogram of Gradient (HOG), Local Binary Pattern (LBP), Darknet53, and Densenet201 architectures, and the Minimum Redundancy Maximum Relevance (mRMR) feature selection method was used for feature selection. The feature maps obtained after the dimensionality reduction process were combined. The Euclidean distance and Peak Signal-to-Noise Ratio (PSNR) were used as similarity measurement methods. Image retrieval was performed using features obtained from CNN architectures and textural-based models to compare the performance of the proposed method. Results: The highest Average Precision (AP) value was reached in the PSNR similarity measurement method with 0.76 in the proposed model. Conclusions: The proposed model is promising for accurately and rapidly determining morphological types of the acromion, thus aiding in the diagnosis and understanding of chronic shoulder disorders. Full article

Background/Objectives: Bladder cancer is a type of cancer that begins in the cells lining the inner surface of the bladder. Although it usually begins in the bladder, it can spread to surrounding tissues, lymph nodes, and other organs in later stages. Early detection of bladder cancer is, therefore, of great importance. Methods: Therefore, this study developed two systems based on classification and Content-Based Image Retrieval (CBIR). The primary purpose of CBIR systems is to compare the visual similarities of a user-provided image with the images in the database and return the most similar ones. CBIR systems offer an effective search and retrieval mechanism by directly using the content of the image data. Results: In the proposed CBIR system, five different CNNs, two different textural-based feature extraction methods, and seven different similarity measurement metrics were tested for feature selection and similarity measurement. Successful feature extraction methods and similarity measurement metrics formed the infrastructure of the developed system. Densenet201 was preferred for feature extraction in the developed system. The cosine metric was used in the proposed CBIR system as a similarity measurement metric, the most successful among seven different metrics. Conclusions: As a result, it was seen that the proposed CBIR model showed the highest success using the Densenet201 model for feature extraction and the Cosine similarity measurement method. Full article

The paper demonstrates a novel methodology for Content-Based Image Retrieval (CBIR), which shifts the focus from conventional domain-specific image queries to more complex text-based query processing. Latent diffusion models are employed to interpret complex textual prompts and address the requirements of effectively interpreting the complex textual query. Latent Diffusion models successfully transform complex textual queries into visually engaging representations, establishing a seamless connection between textual descriptions and visual content. Custom triplet network design is at the heart of our retrieval method. When trained well, a triplet network will represent the generated query image and the different images in the database. The cosine similarity metric is used to assess the similarity between the feature representations in order to find and retrieve the relevant images. Our experiments results show that latent diffusion models can successfully bridge the gap between complex textual prompts for image retrieval without relying on labels or metadata that are attached to database images. This advancement sets the stage for future explorations in image retrieval, leveraging the generative AI capabilities to cater to the ever-evolving demands of big data and complex query interpretations. Full article

The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set within the leaf nodes and the query descriptor to rank them accordingly. Three tree search algorithms are presented to enable a trade-off between search accuracy and speed that allows for substantial gains at the expense of a slightly reduced retrieval accuracy. Our proposed method demonstrates enhancement in image retrieval speed when applied to the CLIP-based model, UNICOM, designed for category-level retrieval, as well as the CNN-based R-GeM model, tailored for particular object retrieval by validating its effectiveness across various domains and backbones. We achieve an 18-times speed improvement while preserving over 99% accuracy when applied to the In-Shop dataset, the largest dataset in the experiments. Full article

Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist pathologists to obtain a faster and more accurate cancer diagnosis. Stain variation between hospitals hampers the performance of CBHIR tools. This paper explores the effects of color normalization (CN) in a recently proposed CBHIR approach to tackle this issue. In this paper, three different CN techniques were used on the CAMELYON17 (CAM17) data set, which is a breast cancer data set. CAM17 consists of images taken using different staining protocols and scanners in five hospitals. Our experiments reveal that a proper CN technique, which can transfer the color version into the most similar median values, has a positive impact on the retrieval performance of the proposed CBHIR framework. According to the obtained results, using CN as a pre-processing step can improve the accuracy of the proposed CBHIR framework to 97% (a $14\%$ increase), compared to working with the original images. Full article

Machine learning algorithms are integrated into computer-aided design (CAD) methodologies to support medical practitioners in diagnosing patient disorders. This research seeks to enhance the accuracy of classifying malaria-infected erythrocytes (RBCs) through the fusion of machine learning algorithms, resulting in a hybrid classifier. The primary phases involve data preprocessing, segmentation, feature extraction, and RBC classification. This paper introduces a novel hybrid machine learning algorithm, employing two combinations of supervised algorithms. The initial combination encompasses stochastic gradient descent (SGD), logistic regression, and decision tree, while the second employs stochastic gradient descent (SGD), Xgboost, and random forest. The proposed approach, implemented using Python programming, presents an innovative hybrid machine learning algorithm. Through a comparative analysis between individual algorithms and the proposed hybrid algorithm, the paper demonstrates heightened accuracy in classifying malaria data, thus aiding medical practitioners in diagnosis. Among these algorithms, SGD, logistic regression, and decision tree yield individual accuracy rates of 90.63%, 92.23%, and 93.43%, respectively, while the hybrid algorithm achieves 95.64% accuracy on the same dataset. The second hybrid algorithm, combining SGD, Xgboost, and random forest, outperforms the initial hybrid version. Individually, these algorithms achieve accuracy rates of 90.63%, 95.86%, and 96.11%. When the proposed hybrid algorithm is applied to the same dataset, accuracy is further enhanced to 96.22%. Full article

This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness ($\widehat{\alpha }$) and scale ($\widehat{\gamma }$) parameters of the $G_I^0$ distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval. Full article

Content-based image retrieval (CBIR) aims to find desired images similar to the image input by the user, and it is extensively used in the real world. Conventional CBIR methods do not consider user preferences since they only determine retrieval results by referring to the degree of resemblance or likeness between the query and potential candidate images. Because of the above reason, a “semantic gap” appears, as the model may not accurately understand the potential intention that a user has included in the query image. In this article, we propose a re-ranking method for CBIR that considers a user’s gaze trace as interactive information to help the model predict the user’s inherent attention. The proposed method uses the user’s gaze trace corresponding to the image obtained from the initial retrieval as the user’s preference information. We introduce image captioning to effectively express the relationship between images and gaze information by generating image captions based on the gaze trace. As a result, we can transform the coordinate data into a text format and explicitly express the semantic information of the images. Finally, image retrieval is performed again using the generated gaze-dependent image captions to obtain images that align more accurately with the user’s preferences or interests. The experimental results on an open image dataset with corresponding gaze traces and human-generated descriptions demonstrate the efficacy or efficiency of the proposed method. Our method considers visual information as the user’s feedback to achieve user-oriented image retrieval. Full article

Medication should be consumed as prescribed with little to zero margins for errors, otherwise consequences could be fatal. Due to the pervasiveness of camera-equipped mobile devices, patients and practitioners can easily take photos of unidentified pills to avert erroneous prescriptions or consumption. This area of research goes under the umbrella of information retrieval and, more specifically, image retrieval or recognition. Several studies have been conducted in the area of image retrieval in order to propose accurate models, i.e., accurately matching an input image with stored ones. Recently, neural networks have been shown to be effective in identifying digital images. This study aims to provide an enhancement to image retrieval in terms of accuracy and efficiency through image segmentation and classification. This paper suggests three neural network (CNN) architectures: two models that are hybrid networks paired with a classification method (CNN+SVM and CNN+kNN) and one ResNet-50 network. We perform various preprocessing steps by using several detection techniques on the selected dataset. We conduct extensive experiments using a real-life dataset obtained from the National Library of Medicine database. The results demonstrate that our proposed model is capable of deriving an accuracy of 90.8%. We also provide a comparison of the above-mentioned three models with some existing methods, and we notice that our proposed CNN+kNN architecture improved the pill image retrieval accuracy by 10% compared to existing models. Full article

The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user’s query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images’ features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention. Full article