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A similarity study of content-based image retrieval system for breast cancer using decision tree.

ABSTRACT: We are developing a decision tree content-based image retrieval (DTCBIR) CADx system to assist radiologists in characterization of breast masses on ultrasound images.Three DTCBIR configurations, including decision tree with boosting (DTb), decision tree with full leaf features (DTL), and decision tree with selected leaf features (DTLs) were compared. For DTb, features of a query mass were combined first into a merged feature score and then masses with similar scores were retrieved. For DTL and DTLs, similar masses were retrieved based on the Euclidean distance between feature vectors of the query and those of selected references. For each DTCBIR configuration, we investigated the use of full feature set and subset of features selected by the stepwise linear discriminant analysis (LDA) and simplex optimization method, resulting in six retrieval methods and selected five, DTb-lda, DTL-lda, DTb-full, DTL-full, and DTLs-full, for the observer study. Three MQSA radiologists rated similarities between the query mass and computer-retrieved three most similar masses using nine-point similarity scale (9 = very similar).For DTb-lda, DTL-lda, DTb-full, DTL-full, and DTLs-full, average A(z) values were 0.90 ± 0.03, 0.85 ± 0.04, 0.87 ± 0.04, 0.79 ± 0.05, and 0.71 ± 0.06, respectively, and average similarity ratings were 5.00, 5.41, 4.96, 5.33, and 5.13, respectively.The DTL-lda is a promising DTCBIR CADx configuration which had simple tree structure, good classification performance, and highest similarity rating.

SUBMITTER: Cho HC

PROVIDER: S-EPMC3537763 | biostudies-other | 2013 Jan

REPOSITORIES: biostudies-other

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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.

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.

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A similarity study of content-based image retrieval system for breast cancer using decision tree.

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