Learning to see the wood for the trees: machine learning,decision trees,and the classification of isolated theropod teeth

Taxonomic identification of fossils based on morphometric data traditionally relies on the use of standard linear models to classify such data. Machine learning and decision trees offer powerful alternative approaches to this problem but are not widely used in palaeontology. Here, we apply these techniques to published morphometric data of isolated theropod teeth in order to explore their utility in tackling taxonomic problems. We chose two published datasets consisting of 886 teeth from 14 taxa and 3020 teeth from 17 taxa, respectively, each with five morphometric variables per tooth. We also explored the effects that missing data have on the final classification accuracy. Our results suggest that machine learning and decision trees yield superior classification results over a wide range of data permutations, with decision trees achieving accuracies of 96% in classifying test data in some cases. Missing data or attempts to generate synthetic data to overcome missing data seriously degrade all classifiers predictive accuracy. The results of our analyses also indicate that using ensemble classifiers combining different classification techniques and the examination of posterior probabilities is a useful aid in checking final class assignments. The application of such techniques to isolated theropod teeth demonstrate that simple morphometric data can be used to yield statistically robust taxonomic classifications and that lower classification accuracy is more likely to reflect preservational limitations of the data or poor application of the methods.     

Identification of Indian butterflies using Deep Convolutional Neural Network

The conventional butterfly identification method is based on their different morphological characters namely wing-venation, color, shape, patterns and through the dissection studies and molecular techniques which are tedious, expensive and highly time-consuming. To overcome the above aforesaid challenges, a new butterfly identification system using butterfly images has been designed to instantly identify the butterfly with high accuracy. In this study, we construct a new butterfly dataset with 34,024 butterfly images belonging to 315 species from India. We propose and prove the effectiveness of new data augmentation techniques on our dataset. To identify butterflies using photographic images, we built eleven new Deep Convolutional Neural Network (DCNN) butterfly classifier models using eleven pre-trained architectures namely ResNet-18, ResNet-34, ResNet-50, ResNet-121, ResNet-152, Alex-Net, DenseNet-121, DenseNet-161, VGG-16, VGG-19 and SqueezeNet-v1.1. The different model's classification results were compared and the proposed technique achieved a maximum top-1 accuracy(94.44%), top-3 accuracy(98.46%) and top-5 accuracy(99.09%) using ResNet-152 model, followed by DenseNet-161 model achieved the top-1 accuracy(94.31%), top-3 accuracy (98.07%) and top-5 accuracy (98.66%). The results suggest that models can be assertively used to identify butterflies in India.     

An effective and fast solution for classification of wood species: A deep transfer learning approach

Classification and recognition of wood species have critical importance in wood trade, industry, and science. Therefore, accurate identification of wood species is a great necessity. Conventional classification and recognition of wood species require knowledge and experience on the anatomy of wood which is time-consuming, cost-ineffective, and destructive. Hence, convolutional neural networks (CNNs) -a deep learning tool- have replaced the conventional methods. In this study, classification of wood species via the WOOD-AUTH dataset and evaluating the performance of various deep learning architectures including ResNet-50, Inception V3, Xception, and VGG19 in classification with transfer learning was investigated in detail. The dataset contains macroscopic images of 12 wood species with three different types of wood sections: cross, radial and tangential. The experimental findings demonstrate that Xception produced a remarkable performance as compared to the other models in this study and the WOOD-AUTH dataset owners, yielding a classification accuracy of 95.88%.     

Automated analysis of high‐content microscopy data with deep learning

Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.     

Robust and simplified machine learning identification of pitfall trap‐collected ground beetles at the continental scale

1. Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.
2. Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.
3. The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.
4. The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.

     

Species‐level image classification with convolutional neural network enables insect identification from habitus images

1. Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground‐dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.
2. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine‐tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade‐off between classification accuracy, precision, and recall and taxonomic resolution.
3. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species.
4. Fine‐tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.
5. Together, species‐level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.

     

Shark detection and classification with machine learning

Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation.We created a database of 53,345 shark images covering 219 species of sharks, and packaged object-detection and image classification models into a Shark Detector bundle. The Shark Detector recognizes and classifies sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: collecting occurrence records from photographs taken by the public or citizen scientists, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity.The Shark Detector can classify 47 species pertaining to 26 genera. It sorted heterogeneous datasets of images sourced from Instagram with 91% accuracy and classified species with 70% accuracy. It located sharks in baited remote footage and YouTube videos with 89% accuracy, and classified located subjects to the species level with 69% accuracy. All data-generation methods were processed without manual interaction.As media-based remote monitoring appears to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.     

环境微生物研究中机器学习算法及应用

微生物在环境中无处不在，它们不仅是生物地球化学循环和环境演化的关键参与者，也在环境监测、生态治理和保护中发挥着重要作用。随着高通量技术的发展，大量微生物数据产生，运用机器学习对环境微生物大数据进行建模和分析，在微生物标志物识别、污染物预测和环境质量预测等领域的科学研究和社会应用方面均具有重要意义。机器学习可分为监督学习和无监督学习2大类。在微生物组学研究当中，无监督学习通过聚类、降维等方法高效地学习输入数据的特征，进而对微生物数据进行整合和归类。监督学习运用有特征和标记的微生物数据集训练模型，在面对只有特征没有标记的数据时可以判断出标记，从而实现对新数据的分类、识别和预测。然而，复杂的机器学习算法通常以牺牲可解释性为代价来重点关注模型预测的准确性。机器学习模型通常可以看作预测特定结果的“黑匣子”，即对模型如何得出预测所知甚少。为了将机器学习更多地运用于微生物组学研究、提高我们提取有价值的微生物信息的能力，深入了解机器学习算法、提高模型的可解释性尤为重要。本文主要介绍在环境微生物领域常用的机器学习算法和基于微生物组数据的机器学习模型的构建步骤，包括特征选择、算法选择、模型构建和评估等，并对各种机器学习模型在环境微生物领域的应用进行综述，深入探究微生物组与周围环境之间的关联，探讨提高模型可解释性的方法，并为未来环境监测、环境健康预测提供科学参考。     

Supervised Machine Learning for Classification of the Electrophysiological Effects of Chronotropic Drugs on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Supervised machine learning can be used to predict which drugs human cardiomyocytes have been exposed to. Using electrophysiological data collected from human cardiomyocytes with known exposure to different drugs, a supervised machine learning algorithm can be trained to recognize and classify cells that have been exposed to an unknown drug. Furthermore, the learning algorithm provides information on the relative contribution of each data parameter to the overall classification. Probabilities and confidence in the accuracy of each classification may also be determined by the algorithm. In this study, the electrophysiological effects of β–adrenergic drugs, propranolol and isoproterenol, on cardiomyocytes derived from human induced pluripotent stem cells (hiPS-CM) were assessed. The electrophysiological data were collected using high temporal resolution 2-photon microscopy of voltage sensitive dyes as a reporter of membrane voltage. The results demonstrate the ability of our algorithm to accurately assess, classify, and predict hiPS-CM membrane depolarization following exposure to chronotropic drugs.     

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.     

An interpretable boosting model to predict side effects of analgesics for osteoarthritis

Background

Osteoarthritis (OA) is the most common disease of arthritis. Analgesics are widely used in the treat of arthritis, which may increase the risk of cardiovascular diseases by 20% to 50% overall.There are few studies on the side effects of OA medication, especially the risk prediction models on side effects of analgesics. In addition, most prediction models do not provide clinically useful interpretable rules to explain the reasoning process behind their predictions. In order to assist OA patients, we use the eXtreme Gradient Boosting (XGBoost) method to balance the accuracy and interpretability of the prediction model.

Results

In this study we used the XGBoost model as a classifier, which is a supervised machine learning method and can predict side effects of analgesics for OA patients and identify high-risk features (RFs) of cardiovascular diseases caused by analgesics. The Electronic Medical Records (EMRs), which were derived from public knee OA studies, were used to train the model. The performance of the XGBoost model is superior to four well-known machine learning algorithms and identifies the risk features from the biomedical literature. In addition the model can provide decision support for using analgesics in OA patients.

Conclusion

Compared with other machine learning methods, we used XGBoost method to predict side effects of analgesics for OA patients from EMRs, and selected the individual informative RFs. The model has good predictability and interpretability, this is valuable for both medical researchers and patients.

     

Multi-view features fusion for birdsong classification

As important members of the ecosystem, birds are good monitors of the ecological environment. Bird recognition, especially birdsong recognition, has attracted more and more attention in the field of artificial intelligence. At present, traditional machine learning and deep learning are widely used in birdsong recognition. Deep learning can not only classify and recognize the spectrums of birdsong, but also be used as a feature extractor. Machine learning is often used to classify and recognize the extracted birdsong handcrafted feature parameters. As the data samples of the classifier, the feature of birdsong directly determines the performance of the classifier. Multi-view features from different methods of feature extraction can obtain more perfect information of birdsong. Therefore, aiming at enriching the representational capacity of single feature and getting a better way to combine features, this paper proposes a birdsong classification model based multi-view features, which combines the deep features extracted by convolutional neural network (CNN) and handcrafted features. Firstly, four kinds of handcrafted features are extracted. Those are wavelet transform (WT) spectrum, Hilbert-Huang transform (HHT) spectrum, short-time Fourier transform (STFT) spectrum and Mel-frequency cepstral coefficients (MFCC). Then CNN is used to extract the deep features from WT, HHT and STFT spectrum, and the minimal-redundancy-maximal-relevance (mRMR) to select optimal features. Finally, three classification models (random forest, support vector machine and multi-layer perceptron) are built with the deep features and handcrafted features, and the probability of classification results of the two types of features are fused as the new features to recognize birdsong. Taking sixteen species of birds as research objects, the experimental results show that the three classifiers obtain the accuracy of 95.49%, 96.25% and 96.16% respectively for the features of the proposed method, which are better than the seven single features and three fused features involved in the experiment. This proposed method effectively combines the deep features and handcrafted features from the perspectives of signal. The fused features can more comprehensively express the information of the bird audio itself, and have higher classification accuracy and lower dimension, which can effectively improve the performance of bird audio classification.     

Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment

HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.     

Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data

BackgroundPrevious epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors.MethodsIn this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children’s parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections.Key findingsOur study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics.ConclusionsWe demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children’s parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.     

Using hierarchical text classification to investigate the utility of machine learning in automating online analyses of wildlife exploitation

Expanding digital data sources, including social media, online news articles and blogs, provide an opportunity to understand better the context and intensity of human-nature interactions, such as wildlife exploitation. However, online searches encompassing large taxonomic groups can generate vast datasets, which can be overwhelming to filter for relevant content without the use of automated tools. The variety of machine learning models available to researchers, and the need for manually labelled training data with an even balance of labels, can make applying these tools challenging. Here, we implement and evaluate a hierarchical text classification pipeline which brings together three binary classification tasks with increasingly specific relevancy criteria. Crucially, the hierarchical approach facilitates the filtering and structuring of a large dataset, of which relevant sources make up a small proportion. Using this pipeline, we also investigate how the accuracy with which text classifiers identify relevant and irrelevant texts is influenced by the use of different models, training datasets, and the classification task. To evaluate our methods, we collected data from Facebook, Twitter, Google and Bing search engines, with the aim of identifying sources documenting the hunting and persecution of bats (Chiroptera). Overall, the ‘state-of-the-art’ transformer-based models were able to identify relevant texts with an average accuracy of 90%, with some classifiers achieving accuracy of >95%. Whilst this demonstrates that application of more advanced models can lead to improved accuracy, comparable performance was achieved by simpler models when applied to longer documents and less ambiguous classification tasks. Hence, the benefits from using more computationally expensive models are dependent on the classification context. We also found that stratification of training data, according to the presence of key search terms, improved classification accuracy for less frequent topics within datasets, and therefore improves the applicability of classifiers to future data collection. Overall, whilst our findings reinforce the usefulness of automated tools for facilitating online analyses in conservation and ecology, they also highlight that the effectiveness and appropriateness of such tools is determined by the nature and volume of data collected, the complexity of the classification task, and the computational resources available to researchers.     

Visual exploration of alternative taxonomies through concepts

A graphical user interface is presented that allows users of taxonomic data to explore concept relationships between conflicting but related taxonomic classifications.Ecological analyses that use taxonomic metadata depend on accurate naming of specimens and taxa, and if the metadata involves several taxonomies, care has to be taken to match concepts between them. To perform this accurately requires expert-defined concept relationships, which are more complex yet more representative than the simple one-to-one mappings found through simple name matching, and can accommodate nomenclatural changes and differences in classification technique (cf ‘lumpers’ versus ‘splitters’). In the SEEK-Taxon (Scientific Environment for Ecological Knowledge) project we aim to help users of taxonomic datasets untangle and understand these relationships through a prototype visual interface which graphically displays these relationship structures, allowing users to comprehend such information and more accurately name their data.     

Classification of group-specific variations in songs within House Wren species using machine learning models

Songbirds have shown variation in vocalizations across different populations and different geographical ranges. Such variations can over time lead to divergence in song characteristics, sometimes referred to as dialects. House Wren (Troglodytes aedon) is one such widely distributed bird species that has shown variation in its song characteristics within different populations. Traditionally, such studies have been conducted using manual approaches for classification. In this work we explore the use of machine learning models that can assist in performing classification of bird songs at a conspecific level. Two machine learning techniques, the random forest and a shallow feed forward neural network, are fed with pre-computed sound features to classify vocal variation in House Wren species across different reported population groups and latitudinal areas. A randomized approach is employed to create balanced subsets of sounds from different locations for repeated classification runs in order to provide a reliable estimate of performance. It is observed that such an automated approach is able to classify variations in songs within House Wren with high accuracy. We were also able to confirm the latitudinal variation of House Wren songs reported in previous studies. Given these results, we believe, such a purely data-driven way of analyzing bird songs in general can provide useful hints to biologists on where to look for interesting patterns in order to understand the evolutionary divergence in song characteristics.     

Mono-temporal and multi-temporal approaches for burnt area detection using Sentinel-2 satellite imagery (a case study of Rokan Hilir Regency,Indonesia)

Accurate and up-to-date information about the burnt area is important in estimating environmental losses, prioritizing rehabilitation areas, and determining future planning strategies. The publicly available medium resolution optical Sentinel-2 satellite data provides a practical and effective solution for burnt area detection. In this study, we proposed two different approaches using mono-temporal and multi-temporal Sentinel-2 satellite imagery to detect burnt areas in Rokan Hilir Regency, Indonesia. The multi-temporal approaches utilized two different ensemble machine learning algorithms (Random Forest and XGBoost) and used six composite spectral indices of the differenced Normalized Burn Ratio (dNBR), differenced Normalized Burn Ratio 2 (dNBR2), differenced Normalized Difference Vegetation Index (dNDVI), differenced Soil Adjusted Vegetation Index (dSAVI), differenced Char Soil Index (dCSI), differenced Burnt area Index for Sentinel-2 (dBAIS2), and differenced Mid-infrared Burn Index (dMIRBI) as model inputs. The burnt areas are labeled by combining hotspots with confidence intervals above 95%, fire spots, and change detection methods. The XGBoost model achieved the best performance with an F1 score of 0.97 and an accuracy of 96%. Furthermore, we use the SHapley Additive exPlanations (SHAP) to quantify the contribution of each feature as well as its correlation with the target class. The dNBR, dMIRBI, and dNBR2 indices contribute the most to the XGBoost model. In comparison, this study also investigates and compares a mono-temporal approach with One-dimensional Convolutional Neural Network (CNN-1D) architecture and the performance obtained is slightly better than both machine learning models. Overall, both mono-temporal and multi-temporal approaches satisfactorily detect the burnt area.     

染色体易位重组位点的自动识别方法研究

染色体易位重组位点的识别对很多染色体遗传性疾病的诊断有着重要的意义.本文基于实际诊断中采集到的24类染色体数据和9号正常与异常染色体数据,构建了一套自动识别染色体易位重组位点的模型和方法.首先,对染色体图像进行预处理,得到了方向梯度直方图特征(HOG)和局部二值模式特征(LBP),构建了基于纹理特征的染色体24分类多通...