Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

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Classification of Cervical Cancer from MRI Images using Multiclass SVM Classifier

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.25.12351 ◽

2018 ◽

Vol 7 (2.25) ◽

pp. 1

Author(s):

Bethanney Janney.J ◽

Umashankar G ◽

Sindu Divakaran ◽

Shelcy Mary Jo ◽

Nancy Basilica.S

Keyword(s):

Cervical Cancer ◽

Region Of Interest ◽

Region Growing ◽

Texture Features ◽

Support Vector ◽

Svm Classifier ◽

Main Body ◽

Birth Canal ◽

Abnormal Growth ◽

Multiclass Svm

Cervical Cancer is the abnormal growth of tissues in the lower, narrow part of the uterus (womb) called the Cervix which connects the main body of the uterus, to the vagina or birth canal. Cervical cancer is one of the most common types of cancer that can be seen in women worldwide. Early detection and proper diagnosis can prevent the severity level and reduce the death rates .In this paper, we have proposed an automated diagnosis system of cervical cancer using texture features and Multiclass SVM (Support Vector Machine) Classifier in MRI images. Initially the MRI images are pre-processed to remove undesirable noises and other effects. After pre-processing, the image is segmented by Region growing method to obtain the region of interest. Texture features are extracted from the segmented region. Almost 22 features are extracted at the region of a segmented area and then passed on to Multiclass SVM Classifier to detect if the given image is cancerous or not. The results of the proposed techniques provide effective results for classifying cancerous and the non-cancerous image.

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Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽

10.3390/agriculture11040371 ◽

2021 ◽

Vol 11 (4) ◽

pp. 371

Author(s):

Yu Jin ◽

Jiawei Guo ◽

Huichun Ye ◽

Jinling Zhao ◽

Wenjiang Huang ◽

...

Keyword(s):

Random Forest ◽

Satellite Imagery ◽

Feature Space ◽

Kappa Coefficient ◽

Classification Model ◽

Support Vector ◽

Textural Feature ◽

Monitoring Accuracy ◽

Areca Catechu ◽

High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

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Lung nodule classification using combination of CNN, second and higher order texture features

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189847 ◽

2021 ◽

pp. 1-9

Author(s):

Amrita Naik ◽

Damodar Reddy Edla

Keyword(s):

Malignant Tumors ◽

Early Stage ◽

Lung Nodule ◽

Texture Features ◽

Svm Classifier ◽

Softmax Classifier ◽

Spatial Features ◽

High Level ◽

Learning Architectures ◽

Nodule Classification

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

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Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Abdominal Radiology ◽

10.1007/s00261-019-02269-9 ◽

2019 ◽

Vol 45 (10) ◽

pp. 3193-3201 ◽

Cited By ~ 3

Author(s):

Yajuan Li ◽

Xialing Huang ◽

Yuwei Xia ◽

Liling Long

Keyword(s):

Machine Learning ◽

Differential Diagnosis ◽

Cell Carcinoma ◽

Area Under The Curve ◽

Image Features ◽

Renal Tumors ◽

Support Vector ◽

Svm Classifier ◽

Renal Oncocytoma ◽

Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

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Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9020109 ◽

2020 ◽

Vol 9 (2) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Bo Cheng ◽

Shiai Cui ◽

Xiaoxiao Ma ◽

Chenbin Liang

Keyword(s):

High Resolution ◽

Extraction Method ◽

Texture Features ◽

Decomposition Methods ◽

Projection Matrix ◽

Training Data ◽

Support Vector ◽

Svm Classifier ◽

Building Area ◽

Adaptive Neighborhood

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

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Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽

10.7717/peerj.6201 ◽

2019 ◽

Vol 7 ◽

pp. e6201 ◽

Cited By ~ 48

Author(s):

Dina A. Ragab ◽

Maha Sharkas ◽

Stephen Marshall ◽

Jinchang Ren

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Input Data ◽

Data Augmentation ◽

Area Under The Curve ◽

Screening Mammography ◽

Support Vector ◽

Svm Classifier ◽

Deep Convolutional Neural Networks ◽

Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

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Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma

Journal of Clinical Medicine ◽

10.3390/jcm9072156 ◽

2020 ◽

Vol 9 (7) ◽

pp. 2156

Author(s):

Mi-ri Kwon ◽

Jung Hee Shin ◽

Hyunjin Park ◽

Hwanho Cho ◽

Eunjin Kim ◽

...

Keyword(s):

Multivariate Analysis ◽

Distant Metastasis ◽

Area Under The Curve ◽

Distant Metastases ◽

Support Vector ◽

Svm Classifier ◽

Gray Level ◽

Thyroid Ultrasound ◽

Widely Invasive ◽

Radiomics Signature

We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis (p < 0.05). Radiomics signature and widely invasive histology were significantly associated with distant metastasis on multivariate analysis (p < 0.01 and p = 0.003). The classifier using the results of the multivariate analysis showed an AUC of 0.93. The radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis of FTC.

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Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Clinical Science ◽

10.1042/cs20170051 ◽

2017 ◽

Vol 131 (13) ◽

pp. 1465-1481 ◽

Cited By ~ 12

Author(s):

Víctor González-Castro ◽

María del C. Valdés Hernández ◽

Francesca M. Chappell ◽

Paul A. Armitage ◽

Stephen Makin ◽

...

Keyword(s):

Goodness Of Fit ◽

Human Performance ◽

Small Vessel Disease ◽

Brain Mri ◽

Area Under The Curve ◽

Local Binary Patterns ◽

Support Vector ◽

Svm Classifier ◽

Perivascular Spaces ◽

Scale Invariant

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

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Content-based image retrieval based on supervised learning and statistical-based moments

Modern Physics Letters B ◽

10.1142/s0217984919502130 ◽

2019 ◽

Vol 33 (19) ◽

pp. 1950213 ◽

Cited By ~ 9

Author(s):

Vibhav Prakash Singh ◽

Rajeev Srivastava ◽

Yadunath Pathak ◽

Shailendra Tiwari ◽

Kuldeep Kaur

Keyword(s):

Image Retrieval ◽

Supervised Learning ◽

Texture Features ◽

Ground Truth ◽

Local Binary Patterns ◽

Content Based Image Retrieval ◽

Support Vector ◽

Svm Classifier ◽

Learning Approaches ◽

Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

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Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study

European Radiology Experimental ◽

10.1186/s41747-019-0121-6 ◽

2019 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Magda Marcon ◽

Alexander Ciritsis ◽

Cristina Rossi ◽

Anton S. Becker ◽

Nicole Berger ◽

...

Keyword(s):

Machine Learning ◽

Pilot Study ◽

Texture Analysis ◽

Area Under The Curve ◽

Texture Features ◽

Breast Ultrasound ◽

Support Vector ◽

Maximum Area ◽

Svm Algorithm ◽

Automated Breast Ultrasound

Abstract Background Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification. Methods This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions. Results Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii). Conclusions TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

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