A similarity study of content-based image retrieval system for breast cancer using decision tree.
Ontology highlight
ABSTRACT:
SUBMITTER: Cho HC
PROVIDER: S-EPMC3537763 | biostudies-other | 2013 Jan
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured.
| S-EPMC6499853 | biostudies-literature
Project description:Acute infections of the middle ear are the most commonly treated childhood diseases. Because complications affect children's language learning and cognitive processes, it is essential to diagnose these diseases in a timely and accurate manner. The prevailing literature suggests that it is difficult to accurately diagnose these infections, even for experienced ear, nose, and throat (ENT) physicians. Advanced care practitioners (e.g., nurse practitioners, physician assistants) serve as first-line providers in many primary care settings and may benefit from additional guidance to appropriately determine the diagnosis and treatment of ear diseases. For this purpose, we designed a content-based image retrieval (CBIR) system (called OtoMatch) for normal, middle ear effusion, and tympanostomy tube conditions, operating on eardrum images captured with a digital otoscope. We present a method that enables the conversion of any convolutional neural network (trained for classification) into an image retrieval model. As a proof of concept, we converted a pre-trained deep learning model into an image retrieval system. We accomplished this by changing the fully connected layers into lookup tables. A database of 454 labeled eardrum images (179 normal, 179 effusion, and 96 tube cases) was used to train and test the system. On a 10-fold cross validation, the proposed method resulted in an average accuracy of 80.58% (SD 5.37%), and maximum F1 score of 0.90 while retrieving the most similar image from the database. These are promising results for the first study to demonstrate the feasibility of developing a CBIR system for eardrum images using the newly proposed methodology.
| S-EPMC7228122 | biostudies-literature
Project description:Medical image analysis for perfect diagnosis of disease has become a very challenging task. Due to improper diagnosis, required medical treatment may be skipped. Proper diagnosis is needed as suspected lesions could be missed by the physician's eye. Hence, this problem can be settled up by better means with the investigation of similar case studies present in the healthcare database. In this context, this paper substantiates an assistive system that would help dermatologists for accurate identification of 23 different kinds of melanoma. For this, 2300 dermoscopic images were used to train the skin-melanoma similar image search system. The proposed system uses feature extraction by assigning dynamic weights to the low-level features based on the individual characteristics of the searched images. Optimal weights are obtained by the newly proposed optimized pair-wise comparison (OPWC) approach. The uniqueness of the proposed approach is that it provides the dynamic weights to the features of the searched image instead of applying static weights. The proposed approach is supported by analytic hierarchy process (AHP) and meta-heuristic optimization algorithms such as particle swarm optimization (PSO), JAYA, genetic algorithm (GA), and gray wolf optimization (GWO). The proposed approach has been tested with images of 23 classes of melanoma and achieved significant precision and recall. Thus, this approach of skin melanoma image search can be used as an expert assistive system to help dermatologists/physicians for accurate identification of different types of melanomas.
| S-EPMC9984623 | biostudies-literature
Project description:Objective and designThe design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated.ResultsThe experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.
| S-EPMC3128405 | biostudies-literature
Project description:Multiscale distance coherence vector algorithm for content-based image retrieval (CBIR) is proposed due to the same descriptor with different shapes and the shortcomings of antinoise performance of the distance coherence vector algorithm. By this algorithm, the image contour curve is evolved by Gaussian function first, and then the distance coherence vector is, respectively, extracted from the contour of the original image and evolved images. Multiscale distance coherence vector was obtained by reasonable weight distribution of the distance coherence vectors of evolved images contour. This algorithm not only is invariable to translation, rotation, and scaling transformation but also has good performance of antinoise. The experiment results show us that the algorithm has a higher recall rate and precision rate for the retrieval of images polluted by noise.
| S-EPMC4032654 | biostudies-other
Project description:IntroductionThis paper presents the preliminary results of a decision-tree analysis of Patient Decision Aids (PDA). PDAs are online or offline tools used to structure health information, elicit relevant values and emphasize the decision as a process, in ways that help patients make more informed health decisions individually or with relevant others.MethodTwenty PDAs are randomly selected from the International Patient Decision Aids Standards (IPDAS) ( https://decisionaid.ohri.ca/AZlist.html ) approved list. An evaluation tool is built bottom-up and top-down and results are described in terms of communicating uncertainty, completeness of the decision tree, ambiguous or misleading phrasing, overall strategies suggested within personal stories.ResultsTwelve of the analyzed PDAs had branches of the decision tree which were not discussed in the tool and 6 had logically ambiguous phrasing. Many tools included dichotomous options, when the option range was wider. Several options were clustered within the "Do not take/Do not do" option and thus the PDA failed to provide all comparisons necessary to make a decision. Some tools employ expressions that do not differentiate between lack of information and known negative effects. Other tools provide unequal amounts or non-comparable bits of information about the options.ConclusionThese results indicate a very loose range of interpretations of what constitutes an option, a treatment, and a treatment option. It thus emphasizes a gap between theory and practice in the evaluation of PDAs. Future developments of PDA evaluation tools should keep track of missing decision tree branches, accurate communication of uncertainty, ambiguity, and lack of knowledge and consider using measures for evaluating the completeness of the option spectrum at an agreed period in time.
| S-EPMC6642566 | biostudies-literature
Project description:Mosquito-borne diseases such as dengue fever and malaria are the top 10 leading causes of death in low-income countries. Control measure for the mosquito population plays an essential role in the fight against the disease. Currently, several intervention strategies; chemical-, biological-, mechanical- and environmental methods remain under development and need further improvement in their effectiveness. Although, a conventional entomological surveillance, required a microscope and taxonomic key for identification by professionals, is a key strategy to evaluate the population growth of these mosquitoes, these techniques are tedious, time-consuming, labor-intensive, and reliant on skillful and well-trained personnel. Here, we proposed an automatic screening, namely the deep metric learning approach and its inference under the image-retrieval process with Euclidean distance-based similarity. We aimed to develop the optimized model to find suitable miners and suggested the robustness of the proposed model by evaluating it with unseen data under a 20-returned image system. During the model development, well-trained ResNet34 are outstanding and no performance difference when comparing five data miners that showed up to 98% in its precision even after testing the model with both image sources: stereomicroscope and mobile phone cameras. The robustness of the proposed-trained model was tested with secondary unseen data which showed different environmental factors such as lighting, image scales, background colors and zoom levels. Nevertheless, our proposed neural network still has great performance with greater than 95% for sensitivity and precision, respectively. Also, the area under the ROC curve given the learning system seems to be practical and empirical with its value greater than 0.960. The results of the study may be used by public health authorities to locate mosquito vectors nearby. If used in the field, our research tool in particular is believed to accurately represent a real-world scenario.
| S-EPMC10313673 | biostudies-literature
Project description:One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.
| S-EPMC4121098 | biostudies-other
Project description:Abstract With the rapid increase in the number of people infected with COVID-19 disease in the entire world, and with the limited medical equipment used to detect it (testing kit), it becomes necessary to provide another detection method that mainly relies on Artificial Intelligence and radiographic Image Analysis to determine the disease infection. In this study, we proposed a diagnosis system that detects the COVID-19 using chest X-ray or computed tomography (CT) scan images knowing that this system does not eliminate the reverse transcription-polymerase chain reaction test but rather complements it. The proposed system consists of the following steps, starting with extracting the image’s features using Visual Words Fusion of ResNet-50 (deep neural network) and Histogram of Oriented Gradient descriptors based on Bag of Visual Word methodology. Then training the Adaptive Boosting classifier to classify the image to COVID-19 or NOTCOVID-19 and finally retrieving the most similar images. We implemented our work on X-ray and CT scan databases, and the experimental results demonstrate the effectiveness of the proposed system. The performance of the classification task in terms of accuracy was as follows: 100% for classifying the input image to X-ray or CT scan, 99.18% for classifying X-ray image to COVID-19 or NOTCOVID-19 and 97.84% for classifying CT scan to COVID-19 or NOTCOVID-19.
| S-EPMC8194842 | biostudies-literature
Project description:Retinal imaging helps to detect retinal and cardiovascular abnormalities. Among these abnormalities, Diabetic Macular Edema (DME) and Age Related Macular Degeneration (AMD), both are frequent retinal degenerative diseases leading to blindness. Content based retinal OCT scan retrieval process makes use of characteristic features to retrieve similar Optical Coherent Tomography (OCT) scans, index-wise, from a database with minimal human intervention. A number of existing methods take care of segmentation and identification of retinal landmarks and pathologies from OCT volumes. As per the literature survey, till date, no papers are there which deal with the retrieval of retinal OCT scans. In this work, we propose a retrieval system for retinal OCT scans which extracts feature maps of both query and database samples from the layer of deep convolutional neural network and compares for their similarity. The Twin network comparison approach exploits deep features without the resource, space and computation exhaustive network training phase. Most of the techniques involving deep network implementation suffer from the drawbacks of data augmentation and resizing. These requirements have been eliminated automatically as part of the Twin network implementation procedure. The system successfully retrieves retinas with similar symptoms from the database of differently affected and unaffected OCT scans. We evaluated different variations of retrieval performances like AMD-Normal, DME-Normal, AMD-DME, AMD-DME-Normal, etc. Execution time optimization has also been achieved as the network used is comparatively shallow and network training is not required. The system retrieves similar scans from a dataset of abnormal and normal OCT scans with a mean average precision of 0.7571 and mean reciprocal rank of 0.9050. Considering all possible variations of retrieval, we achieved overall mean average precision and mean reciprocal rank of 0.631167 and 0.829607, respectively which are also quite notable with rank thresholds of 3, 5 and 7. Experiments show that the method is noticeably successful in retrieving similar OCT volumes. Per image mean average retrieval time is 8.3 sec. Automatic retrieval of retinal OCT volumes for the presence of a particular ailment can help ophthalmologists in the mass screening process. Supplementary Information The online version contains supplementary material available at 10.1007/s12046-021-01701-5.
| S-EPMC8386161 | biostudies-literature